[SPARK-31956][SQL] Do not fail if there is no ambiguous self join

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

This is a followup of #28695 , to fix the problem completely.

The root cause is that, `df("col").as("name")` is not a column reference anymore, and should not have the special column metadata. However, this was broken in ba7adc4#diff-ac415c903887e49486ba542a65eec980L1050-L1053

This PR fixes the regression, by strip the special column metadata in `Column.name`, which is the behavior before #28326 .

### Why are the changes needed?

Fix a regression. We shouldn't fail if there is no ambiguous self-join.

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

Yes, the query in the test can run now.

### How was this patch tested?

updated test

Closes #28783 from cloud-fan/self-join.

Authored-by: Wenchen Fan <[email protected]>
Signed-off-by: Dongjoon Hyun <[email protected]>

Author: cloud-fan

sql/core/src/main/scala/org/apache/spark/sql/Column.scala

@@ -1042,7 +1042,7 @@ class Column(val expr: Expression) extends Logging {
    * @since 2.0.0
    */
   def name(alias: String): Column = withExpr {
-    Alias(expr, alias)()
+    Alias(normalizedExpr(), alias)()
   }
 
   /**

sql/core/src/test/scala/org/apache/spark/sql/DataFrameSelfJoinSuite.scala

@@ -204,14 +204,19 @@ class DataFrameSelfJoinSuite extends QueryTest with SharedSparkSession {
     }
   }
 
-  test("SPARK-28344: don't fail as ambiguous self join when there is no join") {
+  test("SPARK-28344: don't fail if there is no ambiguous self join") {
     withSQLConf(
       SQLConf.FAIL_AMBIGUOUS_SELF_JOIN_ENABLED.key -> "true") {
       val df = Seq(1, 1, 2, 2).toDF("a")
       val w = Window.partitionBy(df("a"))
       checkAnswer(
         df.select(df("a").alias("x"), sum(df("a")).over(w)),
         Seq((1, 2), (1, 2), (2, 4), (2, 4)).map(Row.fromTuple))
+
+      val joined = df.join(spark.range(1)).select($"a")
+      checkAnswer(
+        joined.select(joined("a").alias("x"), sum(joined("a")).over(w)),
+        Seq((1, 2), (1, 2), (2, 4), (2, 4)).map(Row.fromTuple))
     }
   }
 }