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

To support formatted explain for AQE.

### Why are the changes needed?

AQE does not support formatted explain yet. It's good to support it for better user experience, debugging, etc.

Before:
```
== Physical Plan ==
AdaptiveSparkPlan (1)
+- * HashAggregate (unknown)
   +- CustomShuffleReader (unknown)
      +- ShuffleQueryStage (unknown)
         +- Exchange (unknown)
            +- * HashAggregate (unknown)
               +- * Project (unknown)
                  +- * BroadcastHashJoin Inner BuildRight (unknown)
                     :- * LocalTableScan (unknown)
                     +- BroadcastQueryStage (unknown)
                        +- BroadcastExchange (unknown)
                           +- LocalTableScan (unknown)

(1) AdaptiveSparkPlan
Output [4]: [k#7, count(v1)#32L, sum(v1)#33L, avg(v2)#34]
Arguments: HashAggregate(keys=[k#7], functions=[count(1), sum(cast(v1#8 as bigint)), avg(cast(v2#19 as bigint))]), AdaptiveExecutionContext(org.apache.spark.sql.SparkSession104ab57b), [PlanAdaptiveSubqueries(Map())], false
```

After:
```
== Physical Plan ==
 AdaptiveSparkPlan (14)
 +- * HashAggregate (13)
    +- CustomShuffleReader (12)
       +- ShuffleQueryStage (11)
          +- Exchange (10)
             +- * HashAggregate (9)
                +- * Project (8)
                   +- * BroadcastHashJoin Inner BuildRight (7)
                      :- * Project (2)
                      :  +- * LocalTableScan (1)
                      +- BroadcastQueryStage (6)
                         +- BroadcastExchange (5)
                            +- * Project (4)
                               +- * LocalTableScan (3)

 (1) LocalTableScan [codegen id : 2]
 Output [2]: [_1#x, _2#x]
 Arguments: [_1#x, _2#x]

 (2) Project [codegen id : 2]
 Output [2]: [_1#x AS k#x, _2#x AS v1#x]
 Input [2]: [_1#x, _2#x]

 (3) LocalTableScan [codegen id : 1]
 Output [2]: [_1#x, _2#x]
 Arguments: [_1#x, _2#x]

 (4) Project [codegen id : 1]
 Output [2]: [_1#x AS k#x, _2#x AS v2#x]
 Input [2]: [_1#x, _2#x]

 (5) BroadcastExchange
 Input [2]: [k#x, v2#x]
 Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint))), [id=#x]

 (6) BroadcastQueryStage
 Output [2]: [k#x, v2#x]
 Arguments: 0

 (7) BroadcastHashJoin [codegen id : 2]
 Left keys [1]: [k#x]
 Right keys [1]: [k#x]
 Join condition: None

 (8) Project [codegen id : 2]
 Output [3]: [k#x, v1#x, v2#x]
 Input [4]: [k#x, v1#x, k#x, v2#x]

 (9) HashAggregate [codegen id : 2]
 Input [3]: [k#x, v1#x, v2#x]
 Keys [1]: [k#x]
 Functions [3]: [partial_count(1), partial_sum(cast(v1#x as bigint)), partial_avg(cast(v2#x as bigint))]
 Aggregate Attributes [4]: [count#xL, sum#xL, sum#x, count#xL]
 Results [5]: [k#x, count#xL, sum#xL, sum#x, count#xL]

 (10) Exchange
 Input [5]: [k#x, count#xL, sum#xL, sum#x, count#xL]
 Arguments: hashpartitioning(k#x, 5), true, [id=#x]

 (11) ShuffleQueryStage
 Output [5]: [sum#xL, k#x, sum#x, count#xL, count#xL]
 Arguments: 1

 (12) CustomShuffleReader
 Input [5]: [k#x, count#xL, sum#xL, sum#x, count#xL]
 Arguments: coalesced

 (13) HashAggregate [codegen id : 3]
 Input [5]: [k#x, count#xL, sum#xL, sum#x, count#xL]
 Keys [1]: [k#x]
 Functions [3]: [count(1), sum(cast(v1#x as bigint)), avg(cast(v2#x as bigint))]
 Aggregate Attributes [3]: [count(1)#xL, sum(cast(v1#x as bigint))#xL, avg(cast(v2#x as bigint))#x]
 Results [4]: [k#x, count(1)#xL AS count(v1)#xL, sum(cast(v1#x as bigint))#xL AS sum(v1)#xL, avg(cast(v2#x as bigint))#x AS avg(v2)#x]

 (14) AdaptiveSparkPlan
 Output [4]: [k#x, count(v1)#xL, sum(v1)#xL, avg(v2)#x]
 Arguments: isFinalPlan=true
```

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

No, this should be new feature along with AQE in Spark 3.0.

### How was this patch tested?

Added a query file: `explain-aqe.sql` and a unit test.

Closes apache#28271 from Ngone51/support_formatted_explain_for_aqe.

Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 8fbfdb3)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>