[SPARK-38034][SQL] Optimize TransposeWindow rule

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

Optimize the TransposeWindow rule to extend applicable cases and optimize time complexity.
TransposeWindow rule will try to eliminate unnecessary shuffle:

but the function compatiblePartitions will only take the first n elements of the window2 partition sequence, for some cases, this will not take effect, like the case below: 

val df = spark.range(10).selectExpr("id AS a", "id AS b", "id AS c", "id AS d")
df.selectExpr(
    "sum(`d`) OVER(PARTITION BY `b`,`a`) as e",
    "sum(`c`) OVER(PARTITION BY `a`) as f"
  ).explain

Current plan

== Physical Plan ==
*(5) Project [e#10L, f#11L]
+- Window [sum(c#4L) windowspecdefinition(a#2L, specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$())) AS f#11L], [a#2L]
   +- *(4) Sort [a#2L ASC NULLS FIRST], false, 0
      +- Exchange hashpartitioning(a#2L, 200), true, [id=#41]
         +- *(3) Project [a#2L, c#4L, e#10L]
            +- Window [sum(d#5L) windowspecdefinition(b#3L, a#2L, specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$())) AS e#10L], [b#3L, a#2L]
               +- *(2) Sort [b#3L ASC NULLS FIRST, a#2L ASC NULLS FIRST], false, 0
                  +- Exchange hashpartitioning(b#3L, a#2L, 200), true, [id=#33]
                     +- *(1) Project [id#0L AS d#5L, id#0L AS b#3L, id#0L AS a#2L, id#0L AS c#4L]
                        +- *(1) Range (0, 10, step=1, splits=10)

Expected plan:

== Physical Plan ==
*(4) Project [e#924L, f#925L]
+- Window [sum(d#43L) windowspecdefinition(b#41L, a#40L, specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$())) AS e#924L], [b#41L, a#40L]
   +- *(3) Sort [b#41L ASC NULLS FIRST, a#40L ASC NULLS FIRST], false, 0
      +- *(3) Project [d#43L, b#41L, a#40L, f#925L]
         +- Window [sum(c#42L) windowspecdefinition(a#40L, specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$())) AS f#925L], [a#40L]
            +- *(2) Sort [a#40L ASC NULLS FIRST], false, 0
               +- Exchange hashpartitioning(a#40L, 200), true, [id=apache#282]
                  +- *(1) Project [id#38L AS d#43L, id#38L AS b#41L, id#38L AS a#40L, id#38L AS c#42L]
                     +- *(1) Range (0, 10, step=1, splits=10)

Also the permutations method has a O(n!) time complexity, which is very expensive when there are many partition columns, we could try to optimize it.

### Why are the changes needed?

We could apply the rule for more cases, which could improve the execution performance by eliminate unnecessary shuffle, and by reducing the time complexity from O(n!) to O(n2), the performance for the rule itself could improve

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

no

### How was this patch tested?

UT

Closes apache#35334 from constzhou/SPARK-38034_optimize_transpose_window_rule.

Authored-by: xzhou <15210830305@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 0cc331d)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

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

@@ -1176,9 +1176,9 @@ object CollapseWindow extends Rule[LogicalPlan] {
  */
 object TransposeWindow extends Rule[LogicalPlan] {
   private def compatiblePartitions(ps1 : Seq[Expression], ps2: Seq[Expression]): Boolean = {
-    ps1.length < ps2.length && ps2.take(ps1.length).permutations.exists(ps1.zip(_).forall {
-      case (l, r) => l.semanticEquals(r)
-    })
+    ps1.length < ps2.length && ps1.forall { expr1 =>
+      ps2.exists(expr1.semanticEquals)
+    }
   }
 
   private def windowsCompatible(w1: Window, w2: Window): Boolean = {

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/TransposeWindowSuite.scala

@@ -142,4 +142,21 @@ class TransposeWindowSuite extends PlanTest {
     comparePlans(optimized, analyzed)
   }
 
+  test("SPARK-38034: transpose two adjacent windows with compatible partitions " +
+    "which is not a prefix") {
+    val query = testRelation
+      .window(Seq(sum(c).as('sum_a_2)), partitionSpec4, orderSpec2)
+      .window(Seq(sum(c).as('sum_a_1)), partitionSpec3, orderSpec1)
+
+    val analyzed = query.analyze
+    val optimized = Optimize.execute(analyzed)
+
+    val correctAnswer = testRelation
+      .window(Seq(sum(c).as('sum_a_1)), partitionSpec3, orderSpec1)
+      .window(Seq(sum(c).as('sum_a_2)), partitionSpec4, orderSpec2)
+      .select('a, 'b, 'c, 'd, 'sum_a_2, 'sum_a_1)
+
+    comparePlans(optimized, correctAnswer.analyze)
+  }
+
 }