[SUPPORT] org.apache.avro.SchemaParseException: Can't redefine: array When there are Top level variables , Struct and Array[struct] (no complex datatype within array[struct])

### Describe the problem you faced

When storing a data structure with the following layout into a copy-on-write table:
```
root
|-- personDetails: struct (nullable = true)
| |-- id: integer (nullable = false)
|-- idInfo: struct (nullable = true)
| |-- adhaarId: integer (nullable = false)
|-- addressInfo: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- addressId: integer (nullable = false)
|-- employmentInfo: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- employmenCd: integer (nullable = false)
|-- src_load_ts: timestamp (nullable = false)
|-- load_ts: timestamp (nullable = false)
|-- load_dt: date (nullable = false)
```
the first write will succeed, but then subsequent writes will fail with the error included in the stacktrace.

### To Reproduce

Steps to reproduce the behavior:

```
case class Person(personDetails: PersonDetails,
idInfo: IdInfo,
addressInfo: Array[AddressInfo] = Array.empty[AddressInfo],
employmentInfo: Array[EmploymentInfo] = Array.empty[EmploymentInfo])

case class PersonDetails(id: Int)

case class IdInfo(adhaarId: Int)

case class AddressInfo(addressId: Int)

case class EmploymentInfo(employmenCd: Int)

def maskedParquetBugTest(spark: SparkSession): Unit = {

import spark.implicits._

val personDetails1 = PersonDetails(1)
val idInfo1 = IdInfo(1)
val addressInfo1 = AddressInfo(1)
val employmentInfo1 = EmploymentInfo(1)

val item1 = Person(personDetails1, idInfo1, Array(addressInfo1), Array(employmentInfo1))
val parquetBugDs = Seq(item1).toDF()
.withColumn("src_load_ts", current_timestamp())
.withColumn("load_ts", timestampInCst).withColumn("load_dt", to_date(col("load_ts")))

parquetBugDs.printSchema()

writeHudi(parquetBugDs, "parquet_bug_ds",
"load_dt",
"personDetails.id",
"src_load_ts")
}

def writeHudi(ds: DataFrame, tableName: String, partitionPath: String, recordKey: String, precombineKey: String): Unit = {

val hoodieConfigs: util.Map[String, String] = new java.util.HashMap[String, String]
hoodieConfigs.put("hoodie.table.name", tableName)
hoodieConfigs.put("hoodie.datasource.write.keygenerator.class", classOf[SimpleKeyGenerator].getName)
hoodieConfigs.put("hoodie.datasource.write.partitionpath.field", partitionPath)
hoodieConfigs.put("hoodie.datasource.write.recordkey.field", recordKey)
hoodieConfigs.put("hoodie.datasource.write.precombine.field", precombineKey)
hoodieConfigs.put("hoodie.payload.ordering.field", precombineKey)
hoodieConfigs.put("hoodie.index.type", "GLOBAL_SIMPLE")
hoodieConfigs.put("hoodie.insert.shuffle.parallelism", "1")
hoodieConfigs.put("hoodie.upsert.shuffle.parallelism", "1")
hoodieConfigs.put("hoodie.bulkinsert.shuffle.parallelism", "1")
hoodieConfigs.put("hoodie.delete.shuffle.parallelism", "1")
hoodieConfigs.put("hoodie.simple.index.update.partition.path", "false")
hoodieConfigs.put("hoodie.datasource.write.payload.class", classOf[DefaultHoodieRecordPayload].getName)
hoodieConfigs.put("hoodie.datasource.write.hive_style_partitioning", "false")
hoodieConfigs.put("hoodie.datasource.write.table.type", COW_TABLE_TYPE_OPT_VAL)
hoodieConfigs.put("hoodie.datasource.write.row.writer.enable", "true")
hoodieConfigs.put("hoodie.combine.before.upsert", "true")
hoodieConfigs.put("hoodie.datasource.write.keygenerator.consistent.logical.timestamp.enabled", "true")
hoodieConfigs.put("hoodie.schema.on.read.enable", "true")
hoodieConfigs.put("hoodie.datasource.write.reconcile.schema", "true")
hoodieConfigs.put("hoodie.datasource.write.operation", "upsert")

ds.toDF().write.format("hudi").
options(hoodieConfigs).
mode("append").
save(s"/tmp/data/hudi/$tableName")
}

maskedParquetBugTest(spark)

maskedParquetBugTest(spark)
```

### Expected behavior

The second write succeeds.

### Environment Description

Hudi version (hudi-spark3.1-bundle_2.12) : 0.12.2 , 0.12.1, 0.12.0

Spark version : 3.1.3

Hive version : -

Hadoop version : -

Storage (HDFS/S3/GCS..) : Local storage

Running on Docker? (yes/no) : No

### Stack Trace
```
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2303)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2252)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2251)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2251)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1124)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1124)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1124)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2490)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2432)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2421)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:902)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2196)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2217)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2236)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2261)
at org.apache.spark.rdd.RDD.count(RDD.scala:1253)
at org.apache.hudi.HoodieSparkSqlWriter$.commitAndPerformPostOperations(HoodieSparkSqlWriter.scala:693)
at org.apache.hudi.HoodieSparkSqlWriter$.write(HoodieSparkSqlWriter.scala:345)
at org.apache.hudi.DefaultSource.createRelation(DefaultSource.scala:145)
at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:46)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:90)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:180)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:218)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:215)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:176)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:132)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:131)
at org.apache.spark.sql.DataFrameWriter.$anonfun$runCommand$1(DataFrameWriter.scala:989)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:989)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:438)
at org.apache.spark.sql.DataFrameWriter.saveInternal(DataFrameWriter.scala:415)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:293)
at com.test.run.hudi.upsert.complex.utils.HudiParquetBugTest$.writeHudi(HudiParquetBugTest.scala:99)
at com.test.run.hudi.upsert.complex.utils.HudiParquetBugTest$.maskedParquetBugTest(HudiParquetBugTest.scala:68)
at com.test.run.hudi.upsert.complex.utils.HudiParquetBugTest$.delayedEndpoint$com$walmart$hnw$datafoundations$techmod$ingestion$utils$HudiParquetBugTest$1(HudiParquetBugTest.scala:32)
at com.test.run.hudi.upsert.complex.utils.HudiParquetBugTest$delayedInit$body.apply(HudiParquetBugTest.scala:14)
at scala.Function0.apply$mcV$sp(Function0.scala:39)
at scala.Function0.apply$mcV$sp$(Function0.scala:39)
at scala.runtime.AbstractFunction0.apply$mcV$sp(AbstractFunction0.scala:17)
at scala.App.$anonfun$main$1$adapted(App.scala:80)
at scala.collection.immutable.List.foreach(List.scala:431)
at scala.App.main(App.scala:80)
at scala.App.main$(App.scala:78)
at com.test.run.hudi.upsert.complex.utils.HudiParquetBugTest$.main(HudiParquetBugTest.scala:14)
at com.test.run.hudi.upsert.complex.utils.HudiParquetBugTest.main(HudiParquetBugTest.scala)
Caused by: org.apache.hudi.exception.HoodieUpsertException: Error upserting bucketType UPDATE for partition :0
at org.apache.hudi.table.action.commit.BaseSparkCommitActionExecutor.handleUpsertPartition(BaseSparkCommitActionExecutor.java:329)
at org.apache.hudi.table.action.commit.BaseSparkCommitActionExecutor.lambda$mapPartitionsAsRDD$a3ab3c4$1(BaseSparkCommitActionExecutor.java:244)
at org.apache.spark.api.java.JavaRDDLike.$anonfun$mapPartitionsWithIndex$1(JavaRDDLike.scala:102)
at org.apache.spark.api.java.JavaRDDLike.$anonfun$mapPartitionsWithIndex$1$adapted(JavaRDDLike.scala:102)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsWithIndex$2(RDD.scala:915)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsWithIndex$2$adapted(RDD.scala:915)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
at org.apache.spark.rdd.RDD.$anonfun$getOrCompute$1(RDD.scala:386)
at org.apache.spark.storage.BlockManager.$anonfun$doPutIterator$1(BlockManager.scala:1440)
at org.apache.spark.storage.BlockManager.org$apache$spark$storage$BlockManager$$doPut(BlockManager.scala:1350)
at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1414)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:1237)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:384)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:335)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:498)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:501)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.hudi.exception.HoodieException: org.apache.avro.SchemaParseException: Can't redefine: array
at org.apache.hudi.table.action.commit.HoodieMergeHelper.runMerge(HoodieMergeHelper.java:166)
at org.apache.hudi.table.action.commit.BaseSparkCommitActionExecutor.handleUpdateInternal(BaseSparkCommitActionExecutor.java:358)
at org.apache.hudi.table.action.commit.BaseSparkCommitActionExecutor.handleUpdate(BaseSparkCommitActionExecutor.java:349)
at org.apache.hudi.table.action.commit.BaseSparkCommitActionExecutor.handleUpsertPartition(BaseSparkCommitActionExecutor.java:322)
... 28 more
Caused by: org.apache.avro.SchemaParseException: Can't redefine: array
at org.apache.avro.Schema$Names.put(Schema.java:1550)
at org.apache.avro.Schema$NamedSchema.writeNameRef(Schema.java:813)
at org.apache.avro.Schema$RecordSchema.toJson(Schema.java:975)
at org.apache.avro.Schema$ArraySchema.toJson(Schema.java:1137)
at org.apache.avro.Schema$UnionSchema.toJson(Schema.java:1242)
at org.apache.avro.Schema$RecordSchema.fieldsToJson(Schema.java:1003)
at org.apache.avro.Schema$RecordSchema.toJson(Schema.java:987)
at org.apache.avro.Schema.toString(Schema.java:426)
at org.apache.avro.Schema.toString(Schema.java:398)
at org.apache.avro.Schema.toString(Schema.java:389)
at org.apache.parquet.avro.AvroReadSupport.setAvroReadSchema(AvroReadSupport.java:69)
at org.apache.hudi.io.storage.HoodieParquetReader.getRecordIterator(HoodieParquetReader.java:69)
at org.apache.hudi.io.storage.HoodieFileReader.getRecordIterator(HoodieFileReader.java:43)
at org.apache.hudi.table.action.commit.HoodieMergeHelper.runMerge(HoodieMergeHelper.java:149)
```

Reproduce the failure with the maskedParquetBugTest and writeHudi example from the issue, then trace the stack from HoodieSparkSqlWriter and BaseSparkCommitActionExecutor into HoodieMergeHelper. Compare the first and second writes involving the top-level structs and arrays. Done means the second append/upsert succeeds without SchemaParseException: Can't redefine: array.

Written by the indexing model from the issue text.