## What changes were proposed in this pull request?
This is a regression introduced by https://github.com/apache/spark/pull/22104 at Spark 2.4.0.
When we have Python UDF in subquery, we will hit an exception
```
Caused by: java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.AttributeReference cannot be cast to org.apache.spark.sql.catalyst.expressions.PythonUDF
at scala.collection.immutable.Stream.map(Stream.scala:414)
at org.apache.spark.sql.execution.python.EvalPythonExec.$anonfun$doExecute$2(EvalPythonExec.scala:98)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:815)
...
```
https://github.com/apache/spark/pull/22104 turned `ExtractPythonUDFs` from a physical rule to optimizer rule. However, there is a difference between a physical rule and optimizer rule. A physical rule always runs once, an optimizer rule may be applied twice on a query tree even the rule is located in a batch that only runs once.
For a subquery, the `OptimizeSubqueries` rule will execute the entire optimizer on the query plan inside subquery. Later on subquery will be turned to joins, and the optimizer rules will be applied to it again.
Unfortunately, the `ExtractPythonUDFs` rule is not idempotent. When it's applied twice on a query plan inside subquery, it will produce a malformed plan. It extracts Python UDF from Python exec plans.
This PR proposes 2 changes to be double safe:
1. `ExtractPythonUDFs` should skip python exec plans, to make the rule idempotent
2. `ExtractPythonUDFs` should skip subquery
## How was this patch tested?
a new test.
Closes#23248 from cloud-fan/python.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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