### What changes were proposed in this pull request?
Optimise the `toJavaDate()` method of `DateTimeUtils` by:
1. Re-using `rebaseGregorianToJulianDays` optimised by #28067
2. Creating `java.sql.Date` instances from milliseconds in UTC since the epoch instead of date-time fields. This allows to avoid "normalization" inside of  `java.sql.Date`.

Also new benchmark for collecting dates is added to `DateTimeBenchmark`.

### Why are the changes needed?
The changes fix the performance regression of collecting `DATE` values comparing to Spark 2.4 (see `DateTimeBenchmark` in MaxGekk#27):

Spark 2.4.6-SNAPSHOT:
```
To/from Java's date-time:                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------------------------------
From java.sql.Date                                  559            603          38          8.9         111.8       1.0X
Collect dates                                      2306           3221        1558          2.2         461.1       0.2X
```
Before the changes:
```
To/from Java's date-time:                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------------------------------
From java.sql.Date                                 1052           1130          73          4.8         210.3       1.0X
Collect dates                                      3251           4943        1624          1.5         650.2       0.3X
```
After:
```
To/from Java's date-time:                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------------------------------
From java.sql.Date                                  416            419           3         12.0          83.2       1.0X
Collect dates                                      1928           2759        1180          2.6         385.6       0.2X
```

### Does this PR introduce any user-facing change?
No

### How was this patch tested?
- By existing tests suites, in particular, `DateTimeUtilsSuite`, `RebaseDateTimeSuite`, `DateFunctionsSuite`, `DateExpressionsSuite`.
- Re-run `DateTimeBenchmark` in the environment:

| Item | Description |
| ---- | ----|
| Region | us-west-2 (Oregon) |
| Instance | r3.xlarge |
| AMI | ubuntu/images/hvm-ssd/ubuntu-bionic-18.04-amd64-server-20190722.1 (ami-06f2f779464715dc5) |
| Java | OpenJDK 64-Bit Server VM 1.8.0_242 and OpenJDK 64-Bit Server VM 11.0.6+10 |

Closes #28212 from MaxGekk/optimize-toJavaDate.

Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

### What changes were proposed in this pull request?
Optimise the `toJavaDate()` method of `DateTimeUtils` by:
1. Re-using `rebaseGregorianToJulianDays` optimised by #28067
2. Creating `java.sql.Date` instances from milliseconds in UTC since the epoch instead of date-time fields. This allows to avoid "normalization" inside of  `java.sql.Date`.

Also new benchmark for collecting dates is added to `DateTimeBenchmark`.

### Why are the changes needed?
The changes fix the performance regression of collecting `DATE` values comparing to Spark 2.4 (see `DateTimeBenchmark` in MaxGekk#27):

Spark 2.4.6-SNAPSHOT:
```
To/from Java's date-time:                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------------------------------
From java.sql.Date                                  559            603          38          8.9         111.8       1.0X
Collect dates                                      2306           3221        1558          2.2         461.1       0.2X
```
Before the changes:
```
To/from Java's date-time:                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------------------------------
From java.sql.Date                                 1052           1130          73          4.8         210.3       1.0X
Collect dates                                      3251           4943        1624          1.5         650.2       0.3X
```
After:
```
To/from Java's date-time:                 Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------------------------------
From java.sql.Date                                  416            419           3         12.0          83.2       1.0X
Collect dates                                      1928           2759        1180          2.6         385.6       0.2X
```

### Does this PR introduce any user-facing change?
No

### How was this patch tested?
- By existing tests suites, in particular, `DateTimeUtilsSuite`, `RebaseDateTimeSuite`, `DateFunctionsSuite`, `DateExpressionsSuite`.
- Re-run `DateTimeBenchmark` in the environment:

| Item | Description |
| ---- | ----|
| Region | us-west-2 (Oregon) |
| Instance | r3.xlarge |
| AMI | ubuntu/images/hvm-ssd/ubuntu-bionic-18.04-amd64-server-20190722.1 (ami-06f2f779464715dc5) |
| Java | OpenJDK 64-Bit Server VM 1.8.0_242 and OpenJDK 64-Bit Server VM 11.0.6+10 |

Closes #28212 from MaxGekk/optimize-toJavaDate.

Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 744c248)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

### What changes were proposed in this pull request?

As title. This PR is to add code-gen support for LEFT SEMI sort merge join. The main change is to add `semiJoin` code path in `SortMergeJoinExec.doProduce()` and introduce `onlyBufferFirstMatchedRow` in `SortMergeJoinExec.genScanner()`. The latter is for left semi sort merge join without condition. For this kind of query, we don't need to buffer all matched rows, but only the first one (this is same as non-code-gen code path).

Example query:

```
val df1 = spark.range(10).select($"id".as("k1"))
val df2 = spark.range(4).select($"id".as("k2"))
val oneJoinDF = df1.join(df2.hint("SHUFFLE_MERGE"), $"k1" === $"k2", "left_semi")
```

Example of generated code for the query:

```
== Subtree 5 / 5 (maxMethodCodeSize:302; maxConstantPoolSize:156(0.24% used); numInnerClasses:0) ==
*(5) Project [id#0L AS k1#2L]
+- *(5) SortMergeJoin [id#0L], [k2#6L], LeftSemi
   :- *(2) Sort [id#0L ASC NULLS FIRST], false, 0
   :  +- Exchange hashpartitioning(id#0L, 5), ENSURE_REQUIREMENTS, [id=#27]
   :     +- *(1) Range (0, 10, step=1, splits=2)
   +- *(4) Sort [k2#6L ASC NULLS FIRST], false, 0
      +- Exchange hashpartitioning(k2#6L, 5), ENSURE_REQUIREMENTS, [id=apache#33]
         +- *(3) Project [id#4L AS k2#6L]
            +- *(3) Range (0, 4, step=1, splits=2)

Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */   return new GeneratedIteratorForCodegenStage5(references);
/* 003 */ }
/* 004 */
/* 005 */ // codegenStageId=5
/* 006 */ final class GeneratedIteratorForCodegenStage5 extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 007 */   private Object[] references;
/* 008 */   private scala.collection.Iterator[] inputs;
/* 009 */   private scala.collection.Iterator smj_streamedInput_0;
/* 010 */   private scala.collection.Iterator smj_bufferedInput_0;
/* 011 */   private InternalRow smj_streamedRow_0;
/* 012 */   private InternalRow smj_bufferedRow_0;
/* 013 */   private long smj_value_2;
/* 014 */   private org.apache.spark.sql.execution.ExternalAppendOnlyUnsafeRowArray smj_matches_0;
/* 015 */   private long smj_value_3;
/* 016 */   private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] smj_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[2];
/* 017 */
/* 018 */   public GeneratedIteratorForCodegenStage5(Object[] references) {
/* 019 */     this.references = references;
/* 020 */   }
/* 021 */
/* 022 */   public void init(int index, scala.collection.Iterator[] inputs) {
/* 023 */     partitionIndex = index;
/* 024 */     this.inputs = inputs;
/* 025 */     smj_streamedInput_0 = inputs[0];
/* 026 */     smj_bufferedInput_0 = inputs[1];
/* 027 */
/* 028 */     smj_matches_0 = new org.apache.spark.sql.execution.ExternalAppendOnlyUnsafeRowArray(1, 2147483647);
/* 029 */     smj_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 030 */     smj_mutableStateArray_0[1] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 031 */
/* 032 */   }
/* 033 */
/* 034 */   private boolean findNextJoinRows(
/* 035 */     scala.collection.Iterator streamedIter,
/* 036 */     scala.collection.Iterator bufferedIter) {
/* 037 */     smj_streamedRow_0 = null;
/* 038 */     int comp = 0;
/* 039 */     while (smj_streamedRow_0 == null) {
/* 040 */       if (!streamedIter.hasNext()) return false;
/* 041 */       smj_streamedRow_0 = (InternalRow) streamedIter.next();
/* 042 */       long smj_value_0 = smj_streamedRow_0.getLong(0);
/* 043 */       if (false) {
/* 044 */         smj_streamedRow_0 = null;
/* 045 */         continue;
/* 046 */
/* 047 */       }
/* 048 */       if (!smj_matches_0.isEmpty()) {
/* 049 */         comp = 0;
/* 050 */         if (comp == 0) {
/* 051 */           comp = (smj_value_0 > smj_value_3 ? 1 : smj_value_0 < smj_value_3 ? -1 : 0);
/* 052 */         }
/* 053 */
/* 054 */         if (comp == 0) {
/* 055 */           return true;
/* 056 */         }
/* 057 */         smj_matches_0.clear();
/* 058 */       }
/* 059 */
/* 060 */       do {
/* 061 */         if (smj_bufferedRow_0 == null) {
/* 062 */           if (!bufferedIter.hasNext()) {
/* 063 */             smj_value_3 = smj_value_0;
/* 064 */             return !smj_matches_0.isEmpty();
/* 065 */           }
/* 066 */           smj_bufferedRow_0 = (InternalRow) bufferedIter.next();
/* 067 */           long smj_value_1 = smj_bufferedRow_0.getLong(0);
/* 068 */           if (false) {
/* 069 */             smj_bufferedRow_0 = null;
/* 070 */             continue;
/* 071 */           }
/* 072 */           smj_value_2 = smj_value_1;
/* 073 */         }
/* 074 */
/* 075 */         comp = 0;
/* 076 */         if (comp == 0) {
/* 077 */           comp = (smj_value_0 > smj_value_2 ? 1 : smj_value_0 < smj_value_2 ? -1 : 0);
/* 078 */         }
/* 079 */
/* 080 */         if (comp > 0) {
/* 081 */           smj_bufferedRow_0 = null;
/* 082 */         } else if (comp < 0) {
/* 083 */           if (!smj_matches_0.isEmpty()) {
/* 084 */             smj_value_3 = smj_value_0;
/* 085 */             return true;
/* 086 */           } else {
/* 087 */             smj_streamedRow_0 = null;
/* 088 */           }
/* 089 */         } else {
/* 090 */           if (smj_matches_0.isEmpty()) {
/* 091 */             smj_matches_0.add((UnsafeRow) smj_bufferedRow_0);
/* 092 */           }
/* 093 */
/* 094 */           smj_bufferedRow_0 = null;
/* 095 */         }
/* 096 */       } while (smj_streamedRow_0 != null);
/* 097 */     }
/* 098 */     return false; // unreachable
/* 099 */   }
/* 100 */
/* 101 */   protected void processNext() throws java.io.IOException {
/* 102 */     while (findNextJoinRows(smj_streamedInput_0, smj_bufferedInput_0)) {
/* 103 */       long smj_value_4 = -1L;
/* 104 */       smj_value_4 = smj_streamedRow_0.getLong(0);
/* 105 */       scala.collection.Iterator<UnsafeRow> smj_iterator_0 = smj_matches_0.generateIterator();
/* 106 */       boolean smj_hasOutputRow_0 = false;
/* 107 */
/* 108 */       while (!smj_hasOutputRow_0 && smj_iterator_0.hasNext()) {
/* 109 */         InternalRow smj_bufferedRow_1 = (InternalRow) smj_iterator_0.next();
/* 110 */
/* 111 */         smj_hasOutputRow_0 = true;
/* 112 */         ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1);
/* 113 */
/* 114 */         // common sub-expressions
/* 115 */
/* 116 */         smj_mutableStateArray_0[1].reset();
/* 117 */
/* 118 */         smj_mutableStateArray_0[1].write(0, smj_value_4);
/* 119 */         append((smj_mutableStateArray_0[1].getRow()).copy());
/* 120 */
/* 121 */       }
/* 122 */       if (shouldStop()) return;
/* 123 */     }
/* 124 */     ((org.apache.spark.sql.execution.joins.SortMergeJoinExec) references[1] /* plan */).cleanupResources();
/* 125 */   }
/* 126 */
/* 127 */ }
```

### Why are the changes needed?

Improve query CPU performance. Test with one query:

```
 def sortMergeJoin(): Unit = {
    val N = 2 << 20
    codegenBenchmark("left semi sort merge join", N) {
      val df1 = spark.range(N).selectExpr(s"id * 2 as k1")
      val df2 = spark.range(N).selectExpr(s"id * 3 as k2")
      val df = df1.join(df2, col("k1") === col("k2"), "left_semi")
      assert(df.queryExecution.sparkPlan.find(_.isInstanceOf[SortMergeJoinExec]).isDefined)
      df.noop()
    }
  }
```

Seeing 30% of run-time improvement:

```
Running benchmark: left semi sort merge join
  Running case: left semi sort merge join code-gen off
  Stopped after 2 iterations, 1369 ms
  Running case: left semi sort merge join code-gen on
  Stopped after 5 iterations, 2743 ms

Java HotSpot(TM) 64-Bit Server VM 1.8.0_181-b13 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU  2.40GHz
left semi sort merge join:                Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------------------------------
left semi sort merge join code-gen off              676            685          13          3.1         322.2       1.0X
left semi sort merge join code-gen on               524            549          32          4.0         249.7       1.3X
```

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Added unit test in `WholeStageCodegenSuite.scala` and `ExistenceJoinSuite.scala`.

Closes apache#32528 from c21/smj-left-semi.

Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

### What changes were proposed in this pull request?

As title. This PR is to add code-gen support for LEFT ANTI sort merge join. The main change is to extract `loadStreamed` in `SortMergeJoinExec.doProduce()`. That is to set all columns values for streamed row, when the streamed row has no output row.

Example query:

```
val df1 = spark.range(10).select($"id".as("k1"))
val df2 = spark.range(4).select($"id".as("k2"))
df1.join(df2.hint("SHUFFLE_MERGE"), $"k1" === $"k2", "left_anti")
```

Example generated code:

```
== Subtree 5 / 5 (maxMethodCodeSize:296; maxConstantPoolSize:156(0.24% used); numInnerClasses:0) ==
*(5) Project [id#0L AS k1#2L]
+- *(5) SortMergeJoin [id#0L], [k2#6L], LeftAnti
   :- *(2) Sort [id#0L ASC NULLS FIRST], false, 0
   :  +- Exchange hashpartitioning(id#0L, 5), ENSURE_REQUIREMENTS, [id=#27]
   :     +- *(1) Range (0, 10, step=1, splits=2)
   +- *(4) Sort [k2#6L ASC NULLS FIRST], false, 0
      +- Exchange hashpartitioning(k2#6L, 5), ENSURE_REQUIREMENTS, [id=apache#33]
         +- *(3) Project [id#4L AS k2#6L]
            +- *(3) Range (0, 4, step=1, splits=2)

Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */   return new GeneratedIteratorForCodegenStage5(references);
/* 003 */ }
/* 004 */
/* 005 */ // codegenStageId=5
/* 006 */ final class GeneratedIteratorForCodegenStage5 extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 007 */   private Object[] references;
/* 008 */   private scala.collection.Iterator[] inputs;
/* 009 */   private scala.collection.Iterator smj_streamedInput_0;
/* 010 */   private scala.collection.Iterator smj_bufferedInput_0;
/* 011 */   private InternalRow smj_streamedRow_0;
/* 012 */   private InternalRow smj_bufferedRow_0;
/* 013 */   private long smj_value_2;
/* 014 */   private org.apache.spark.sql.execution.ExternalAppendOnlyUnsafeRowArray smj_matches_0;
/* 015 */   private long smj_value_3;
/* 016 */   private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] smj_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[2];
/* 017 */
/* 018 */   public GeneratedIteratorForCodegenStage5(Object[] references) {
/* 019 */     this.references = references;
/* 020 */   }
/* 021 */
/* 022 */   public void init(int index, scala.collection.Iterator[] inputs) {
/* 023 */     partitionIndex = index;
/* 024 */     this.inputs = inputs;
/* 025 */     smj_streamedInput_0 = inputs[0];
/* 026 */     smj_bufferedInput_0 = inputs[1];
/* 027 */
/* 028 */     smj_matches_0 = new org.apache.spark.sql.execution.ExternalAppendOnlyUnsafeRowArray(1, 2147483647);
/* 029 */     smj_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 030 */     smj_mutableStateArray_0[1] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 031 */
/* 032 */   }
/* 033 */
/* 034 */   private boolean findNextJoinRows(
/* 035 */     scala.collection.Iterator streamedIter,
/* 036 */     scala.collection.Iterator bufferedIter) {
/* 037 */     smj_streamedRow_0 = null;
/* 038 */     int comp = 0;
/* 039 */     while (smj_streamedRow_0 == null) {
/* 040 */       if (!streamedIter.hasNext()) return false;
/* 041 */       smj_streamedRow_0 = (InternalRow) streamedIter.next();
/* 042 */       long smj_value_0 = smj_streamedRow_0.getLong(0);
/* 043 */       if (false) {
/* 044 */         if (!smj_matches_0.isEmpty()) {
/* 045 */           smj_matches_0.clear();
/* 046 */         }
/* 047 */         return false;
/* 048 */
/* 049 */       }
/* 050 */       if (!smj_matches_0.isEmpty()) {
/* 051 */         comp = 0;
/* 052 */         if (comp == 0) {
/* 053 */           comp = (smj_value_0 > smj_value_3 ? 1 : smj_value_0 < smj_value_3 ? -1 : 0);
/* 054 */         }
/* 055 */
/* 056 */         if (comp == 0) {
/* 057 */           return true;
/* 058 */         }
/* 059 */         smj_matches_0.clear();
/* 060 */       }
/* 061 */
/* 062 */       do {
/* 063 */         if (smj_bufferedRow_0 == null) {
/* 064 */           if (!bufferedIter.hasNext()) {
/* 065 */             smj_value_3 = smj_value_0;
/* 066 */             return !smj_matches_0.isEmpty();
/* 067 */           }
/* 068 */           smj_bufferedRow_0 = (InternalRow) bufferedIter.next();
/* 069 */           long smj_value_1 = smj_bufferedRow_0.getLong(0);
/* 070 */           if (false) {
/* 071 */             smj_bufferedRow_0 = null;
/* 072 */             continue;
/* 073 */           }
/* 074 */           smj_value_2 = smj_value_1;
/* 075 */         }
/* 076 */
/* 077 */         comp = 0;
/* 078 */         if (comp == 0) {
/* 079 */           comp = (smj_value_0 > smj_value_2 ? 1 : smj_value_0 < smj_value_2 ? -1 : 0);
/* 080 */         }
/* 081 */
/* 082 */         if (comp > 0) {
/* 083 */           smj_bufferedRow_0 = null;
/* 084 */         } else if (comp < 0) {
/* 085 */           if (!smj_matches_0.isEmpty()) {
/* 086 */             smj_value_3 = smj_value_0;
/* 087 */             return true;
/* 088 */           } else {
/* 089 */             return false;
/* 090 */           }
/* 091 */         } else {
/* 092 */           if (smj_matches_0.isEmpty()) {
/* 093 */             smj_matches_0.add((UnsafeRow) smj_bufferedRow_0);
/* 094 */           }
/* 095 */
/* 096 */           smj_bufferedRow_0 = null;
/* 097 */         }
/* 098 */       } while (smj_streamedRow_0 != null);
/* 099 */     }
/* 100 */     return false; // unreachable
/* 101 */   }
/* 102 */
/* 103 */   protected void processNext() throws java.io.IOException {
/* 104 */     while (smj_streamedInput_0.hasNext()) {
/* 105 */       findNextJoinRows(smj_streamedInput_0, smj_bufferedInput_0);
/* 106 */
/* 107 */       long smj_value_4 = -1L;
/* 108 */       smj_value_4 = smj_streamedRow_0.getLong(0);
/* 109 */       scala.collection.Iterator<UnsafeRow> smj_iterator_0 = smj_matches_0.generateIterator();
/* 110 */
/* 111 */       boolean wholestagecodegen_hasOutputRow_0 = false;
/* 112 */
/* 113 */       while (!wholestagecodegen_hasOutputRow_0 && smj_iterator_0.hasNext()) {
/* 114 */         InternalRow smj_bufferedRow_1 = (InternalRow) smj_iterator_0.next();
/* 115 */
/* 116 */         wholestagecodegen_hasOutputRow_0 = true;
/* 117 */       }
/* 118 */
/* 119 */       if (!wholestagecodegen_hasOutputRow_0) {
/* 120 */         // load all values of streamed row, because the values not in join condition are not
/* 121 */         // loaded yet.
/* 122 */
/* 123 */         ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1);
/* 124 */
/* 125 */         // common sub-expressions
/* 126 */
/* 127 */         smj_mutableStateArray_0[1].reset();
/* 128 */
/* 129 */         smj_mutableStateArray_0[1].write(0, smj_value_4);
/* 130 */         append((smj_mutableStateArray_0[1].getRow()).copy());
/* 131 */
/* 132 */       }
/* 133 */       if (shouldStop()) return;
/* 134 */     }
/* 135 */     ((org.apache.spark.sql.execution.joins.SortMergeJoinExec) references[1] /* plan */).cleanupResources();
/* 136 */   }
/* 137 */
/* 138 */ }
```

### Why are the changes needed?

Improve the query CPU performance.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Added unit test in `WholeStageCodegenSuite.scala`, and existed unit test in `ExistenceJoinSuite.scala`.

Closes apache#32547 from c21/smj-left-anti.

Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>

…n Aggregate

### What changes were proposed in this pull request?

This PR implements the implicit lateral column alias on `Aggregate` case. For example,
```sql
-- LCA in Aggregate. The avg_salary references an attribute defined by a previous alias
SELECT dept, average(salary) AS avg_salary, avg_salary + average(bonus)
FROM employee
GROUP BY dept
```

The high level implementation idea is to insert the `Project` node above, and falling back to the resolution of lateral alias of Project code path in the last PR.

* Phase 1: recognize resolved lateral alias, wrap the attributes referencing them with `LateralColumnAliasReference`
* Phase 2: when the `Aggregate` operator is resolved, it goes through the whole aggregation list, extracts the aggregation expressions and grouping expressions to keep them in this `Aggregate` node, and add a `Project` above with the original output. It doesn't do anything on `LateralColumnAliasReference`, but completely leave it to the Project in the future turns of this rule.

Example:
```
 // Before rewrite:
 Aggregate [dept#14] [dept#14 AS a#12, 'a + 1, avg(salary#16) AS b#13, 'b + avg(bonus#17)]
 +- Child [dept#14,name#15,salary#16,bonus#17]

 // After phase 1:
 Aggregate [dept#14] [dept#14 AS a#12, lca(a) + 1, avg(salary#16) AS b#13, lca(b) + avg(bonus#17)]
 +- Child [dept#14,name#15,salary#16,bonus#17]

 // After phase 2:
 Project [dept#14 AS a#12, lca(a) + 1, avg(salary)#26 AS b#13, lca(b) + avg(bonus)#27]
 +- Aggregate [dept#14] [avg(salary#16) AS avg(salary)#26, avg(bonus#17) AS avg(bonus)#27, dept#14]
     +- Child [dept#14,name#15,salary#16,bonus#17]

 // Now the problem falls back to the lateral alias resolution in Project.
 // After future rounds of this rule:
 Project [a#12, a#12 + 1, b#13, b#13 + avg(bonus)#27]
 +- Project [dept#14 AS a#12, avg(salary)#26 AS b#13]
    +- Aggregate [dept#14] [avg(salary#16) AS avg(salary)#26, avg(bonus#17) AS avg(bonus)#27, dept#14]
       +- Child [dept#14,name#15,salary#16,bonus#17]
```

Similar as the last PR (apache#38776), because lateral column alias has higher resolution priority than outer reference, it will try to resolve an `OuterReference` using lateral column alias, similar as an `UnresolvedAttribute`. If success, it strips `OuterReference` and also wraps it with `LateralColumnAliasReference`.

### Why are the changes needed?
Similar as stated in apache#38776.

### Does this PR introduce _any_ user-facing change?

Yes, as shown in the above example, it will be able to resolve lateral column alias in Aggregate.

### How was this patch tested?

Existing tests and newly added tests.

Closes apache#39040 from anchovYu/SPARK-27561-agg.

Authored-by: Xinyi Yu <xinyi.yu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

…onnect

### What changes were proposed in this pull request?
Implement Arrow-optimized Python UDFs in Spark Connect.

Please see apache#39384 for motivation and  performance improvements of Arrow-optimized Python UDFs.

### Why are the changes needed?
Parity with vanilla PySpark.

### Does this PR introduce _any_ user-facing change?
Yes. In Spark Connect Python Client, users can:

1. Set `useArrow` parameter True to enable Arrow optimization for a specific Python UDF.

```sh
>>> df = spark.range(2)
>>> df.select(udf(lambda x : x + 1, useArrow=True)('id')).show()
+------------+
|<lambda>(id)|
+------------+
|           1|
|           2|
+------------+

# ArrowEvalPython indicates Arrow optimization
>>> df.select(udf(lambda x : x + 1, useArrow=True)('id')).explain()
== Physical Plan ==
*(2) Project [pythonUDF0#18 AS <lambda>(id)#16]
+- ArrowEvalPython [<lambda>(id#14L)#15], [pythonUDF0#18], 200
   +- *(1) Range (0, 2, step=1, splits=1)
```

2. Enable `spark.sql.execution.pythonUDF.arrow.enabled` Spark Conf to make all Python UDFs Arrow-optimized.

```sh
>>> spark.conf.set("spark.sql.execution.pythonUDF.arrow.enabled", True)
>>> df.select(udf(lambda x : x + 1)('id')).show()
+------------+
|<lambda>(id)|
+------------+
|           1|
|           2|
+------------+

# ArrowEvalPython indicates Arrow optimization
>>> df.select(udf(lambda x : x + 1)('id')).explain()
== Physical Plan ==
*(2) Project [pythonUDF0#30 AS <lambda>(id)#28]
+- ArrowEvalPython [<lambda>(id#26L)#27], [pythonUDF0#30], 200
   +- *(1) Range (0, 2, step=1, splits=1)

```

### How was this patch tested?
Parity unit tests.

Closes apache#40725 from xinrong-meng/connect_arrow_py_udf.

Authored-by: Xinrong Meng <xinrong@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>