[SPARK-46541][SQL][CONNECT] Fix the ambiguous column reference in self join

[zhengruifeng]

What changes were proposed in this pull request?

fix the logic of ambiguous column detection in spark connect

Why are the changes needed?

In [24]: df1 = spark.range(10).withColumn("a", sf.lit(0))

In [25]: df2 = df1.withColumnRenamed("a", "b")

In [26]: df1.join(df2, df1["a"] == df2["b"])
Out[26]: 23/12/22 09:33:28 ERROR ErrorUtils: Spark Connect RPC error during: analyze. UserId: ruifeng.zheng. SessionId: eaa2161f-4b64-4dbf-9809-af6b696d3005.
org.apache.spark.sql.AnalysisException: [AMBIGUOUS_COLUMN_REFERENCE] Column a is ambiguous. It's because you joined several DataFrame together, and some of these DataFrames are the same.
This column points to one of the DataFrame but Spark is unable to figure out which one.
Please alias the DataFrames with different names via DataFrame.alias before joining them,
and specify the column using qualified name, e.g. df.alias("a").join(df.alias("b"), col("a.id") > col("b.id")). SQLSTATE: 42702
	at org.apache.spark.sql.catalyst.analysis.ColumnResolutionHelper.findPlanById(ColumnResolutionHelper.scala:555)
	at 

Does this PR introduce any user-facing change?

yes, fix a bug

How was this patch tested?

added ut

Was this patch authored or co-authored using generative AI tooling?

no

[zhengruifeng]

cc @cloud-fan I think it is ready for the initial review

[zhengruifeng]

need to filter with output+metadataOutput, otherwise metadata column's tests will fail

[cloud-fan]

we should merge this with the other resolveUnresolvedAttributeByPlanId. The overall algorithm should be

1. bottom-up traverse the plan tree to find the matching df plan.
2. resolve the column and propogate it up
3. during propogation, prune the resolved columns with plan's output. It should happen for each plan node, not just the top plan node's children.

[zhengruifeng]

thanks @cloud-fan for guide and review

merged to master