[SPARK-31945][SQL][PYSPARK] Enable cache for the same Python function

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

This PR proposes to make `PythonFunction` holds `Seq[Byte]` instead of `Array[Byte]` to be able to compare if the byte array has the same values for the cache manager.

### Why are the changes needed?

Currently the cache manager doesn't use the cache for `udf` if the `udf` is created again even if the functions is the same.

```py
>>> func = lambda x: x

>>> df = spark.range(1)
>>> df.select(udf(func)("id")).cache()
```
```py
>>> df.select(udf(func)("id")).explain()
== Physical Plan ==
*(2) Project [pythonUDF0#14 AS <lambda>(id)apache#12]
+- BatchEvalPython [<lambda>(id#0L)], [pythonUDF0#14]
 +- *(1) Range (0, 1, step=1, splits=12)
```

This is because `PythonFunction` holds `Array[Byte]`, and `equals` method of array equals only when the both array is the same instance.

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

Yes, if the user reuse the Python function for the UDF, the cache manager will detect the same function and use the cache for it.

### How was this patch tested?

I added a test case and manually.

```py
>>> df.select(udf(func)("id")).explain()
== Physical Plan ==
InMemoryTableScan [<lambda>(id)apache#12]
   +- InMemoryRelation [<lambda>(id)apache#12], StorageLevel(disk, memory, deserialized, 1 replicas)
         +- *(2) Project [pythonUDF0#5 AS <lambda>(id)#3]
            +- BatchEvalPython [<lambda>(id#0L)], [pythonUDF0#5]
               +- *(1) Range (0, 1, step=1, splits=12)
```

Closes apache#28774 from ueshin/issues/SPARK-31945/udf_cache.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>

core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala

@@ -74,13 +74,25 @@ private[spark] class PythonRDD(
  * runner.
  */
 private[spark] case class PythonFunction(
-    command: Array[Byte],
+    command: Seq[Byte],
     envVars: JMap[String, String],
     pythonIncludes: JList[String],
     pythonExec: String,
     pythonVer: String,
     broadcastVars: JList[Broadcast[PythonBroadcast]],
-    accumulator: PythonAccumulatorV2)
+    accumulator: PythonAccumulatorV2) {
+
+  def this(
+      command: Array[Byte],
+      envVars: JMap[String, String],
+      pythonIncludes: JList[String],
+      pythonExec: String,
+      pythonVer: String,
+      broadcastVars: JList[Broadcast[PythonBroadcast]],
+      accumulator: PythonAccumulatorV2) = {
+    this(command.toSeq, envVars, pythonIncludes, pythonExec, pythonVer, broadcastVars, accumulator)
+  }
+}
 
 /**
  * A wrapper for chained Python functions (from bottom to top).

core/src/main/scala/org/apache/spark/api/python/PythonRunner.scala

@@ -613,7 +613,7 @@ private[spark] class PythonRunner(funcs: Seq[ChainedPythonFunctions])
       protected override def writeCommand(dataOut: DataOutputStream): Unit = {
         val command = funcs.head.funcs.head.command
         dataOut.writeInt(command.length)
-        dataOut.write(command)
+        dataOut.write(command.toArray)
       }
 
       protected override def writeIteratorToStream(dataOut: DataOutputStream): Unit = {

python/pyspark/sql/tests/test_udf.py

@@ -642,6 +642,15 @@ def f(*a):
         r = df.select(fUdf(*df.columns))
         self.assertEqual(r.first()[0], "success")
 
+    def test_udf_cache(self):
+        func = lambda x: x
+
+        df = self.spark.range(1)
+        df.select(udf(func)("id")).cache()
+
+        self.assertEqual(df.select(udf(func)("id"))._jdf.queryExecution()
+                         .withCachedData().getClass().getSimpleName(), 'InMemoryRelation')
+
 
 class UDFInitializationTests(unittest.TestCase):
     def tearDown(self):

sql/core/src/main/scala/org/apache/spark/sql/execution/python/PythonUDFRunner.scala

@@ -104,7 +104,7 @@ object PythonUDFRunner {
       dataOut.writeInt(chained.funcs.length)
       chained.funcs.foreach { f =>
         dataOut.writeInt(f.command.length)
-        dataOut.write(f.command)
+        dataOut.write(f.command.toArray)
       }
     }
   }