[SPARK-31958][SQL] normalize special floating numbers in subquery

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

This is a followup of #23388 .

#23388 has an issue: it doesn't handle subquery expressions and assumes they will be turned into joins. However, this is not true for non-correlated subquery expressions.

This PR fixes this issue. It now doesn't skip `Subquery`, and subquery expressions will be handled by `OptimizeSubqueries`, which runs the optimizer with the subquery.

Note that, correlated subquery expressions will be handled twice: once in `OptimizeSubqueries`, once later when it becomes join. This is OK as `NormalizeFloatingNumbers` is idempotent now.

### Why are the changes needed?

fix a bug

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

yes, see the newly added test.

### How was this patch tested?

new test

Closes #28785 from cloud-fan/normalize.

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

Author: cloud-fan

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/NormalizeFloatingNumbers.scala

@@ -56,10 +56,6 @@ import org.apache.spark.sql.types._
 object NormalizeFloatingNumbers extends Rule[LogicalPlan] {
 
   def apply(plan: LogicalPlan): LogicalPlan = plan match {
-    // A subquery will be rewritten into join later, and will go through this rule
-    // eventually. Here we skip subquery, as we only need to run this rule once.
-    case _: Subquery => plan
-
     case _ => plan transform {
       case w: Window if w.partitionSpec.exists(p => needNormalize(p)) =>
         // Although the `windowExpressions` may refer to `partitionSpec` expressions, we don't need

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

@@ -3503,6 +3503,24 @@ class SQLQuerySuite extends QueryTest with SharedSparkSession with AdaptiveSpark
     checkAnswer(sql("select CAST(-32768 as short) DIV CAST (-1 as short)"),
       Seq(Row(Short.MinValue.toLong * -1)))
   }
+
+  test("normalize special floating numbers in subquery") {
+    withTempView("v1", "v2", "v3") {
+      Seq(-0.0).toDF("d").createTempView("v1")
+      Seq(0.0).toDF("d").createTempView("v2")
+      spark.range(2).createTempView("v3")
+
+      // non-correlated subquery
+      checkAnswer(sql("SELECT (SELECT v1.d FROM v1 JOIN v2 ON v1.d = v2.d)"), Row(-0.0))
+      // correlated subquery
+      checkAnswer(
+        sql(
+          """
+            |SELECT id FROM v3 WHERE EXISTS
+            |  (SELECT v1.d FROM v1 JOIN v2 ON v1.d = v2.d WHERE id > 0)
+            |""".stripMargin), Row(1))
+    }
+  }
 }
 
 case class Foo(bar: Option[String])