[BUG] Hudi 0.11.x Spark SQL CTAS requires `hoodie` properties in addition to table properties

**Describe the problem you faced**

[Hudi guide](https://hudi.apache.org/docs/0.11.1/table_management#spark-create-table) for Spark SQL Create table states that record key, precombine key and partition keys should be specified with special arguments `primaryKey`, `preCombineField` and `partition by ()`.

**To Reproduce**

Steps to reproduce the behavior:

*Failure scenario:*
1. Run SQL as per docs:
```sql
CREATE TABLE {target_db}.{target_table_name} using hudi
location 's3://{target_bucket_name}/{target_table_name}'
options (
type = 'cow', primaryKey='{pk}', preCombineField='{precombine_field}',

hoodie.table.name='{target_table_name}',
hoodie.datasource.write.operation='upsert',
hoodie.datasource.write.table.name='{target_table_name}',
)
partitioned by ({partition_fields})
AS
SELECT *
FROM {source_db}.{source_table};
```
Throws `Caused by: org.apache.avro.AvroRuntimeException: Not a valid schema field: ts` . Notice that this is using the default value for `preCombineField=ts` instead of user specified custom field. This indicates that `preCombineField` option has no effect on Hudi when creating tables using Spark SQL.

*Success Scenario:*
1. run following SQL with `hoodie` properties specified in addition to SQL options
```
CREATE TABLE {target_db}.{target_table_name} using hudi
location 's3://{target_bucket_name}/{target_table_name}'
options (
type = 'cow', primaryKey='{pk}', preCombineField='{precombine_field}',

hoodie.table.name='{target_table_name}',
hoodie.datasource.write.operation='upsert',
hoodie.datasource.write.table.name='{target_table_name}',

hoodie.datasource.write.recordkey.field='{primary_key}',
hoodie.datasource.write.precombine.field='{precombine_field}',
hoodie.datasource.write.partitionpath.field='{partition_fields}',
hoodie.datasource.write.keygenerator.class='org.apache.hudi.keygen.ComplexKeyGenerator',
)
partitioned by ({partition_fields})
AS
SELECT *
FROM {source_db}.{source_table};
```
this is successful because both `primaryKey` and `hoodie.datasource.write.recordkey.field` are specified. both `preCombineField` and `hoodie.datasource.write.precombine.field` are specified and both `partitioned by ()` and `hoodie.datasource.write.partitionpath.field` are specified.

**Expected behavior**

Failure scenario above should be successful.

**Environment Description**

[EMR 6.7.0](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/release-version-670.html)

* Hudi version : 0.11.1
* Spark version : 3.2.1
* Hive version : 3.1.3
* Hadoop version :
* Storage (HDFS/S3/GCS..) : S3
* Running on Docker? (yes/no) : no

**Additional context**

Add any other context about the problem here.

**Stacktrace**

```
Traceback (most recent call last):
File "/tmp/spark-618dec17-fd89-49da-abff-8893b20c3183/hudi_remodeller.py", line 66, in
args.primary_key, args.precombine_field, args.partition_fields, args.region)
File "/tmp/spark-618dec17-fd89-49da-abff-8893b20c3183/hudi_remodeller.py", line 45, in remodel_table
spark.sql(sql)
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/session.py", line 723, in sql
File "/usr/lib/spark/python/lib/py4j-0.10.9.3-src.zip/py4j/java_gateway.py", line 1322, in __call__
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 111, in deco
File "/usr/lib/spark/python/lib/py4j-0.10.9.3-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o90.sql.
: org.apache.hudi.exception.HoodieUpsertException: Failed to upsert for commit time 20221129132817905
at org.apache.hudi.table.action.commit.BaseWriteHelper.write(BaseWriteHelper.java:64)
at org.apache.hudi.table.action.commit.SparkUpsertCommitActionExecutor.execute(SparkUpsertCommitActionExecutor.java:45)
at org.apache.hudi.table.HoodieSparkCopyOnWriteTable.upsert(HoodieSparkCopyOnWriteTable.java:113)
at org.apache.hudi.table.HoodieSparkCopyOnWriteTable.upsert(HoodieSparkCopyOnWriteTable.java:97)
at org.apache.hudi.client.SparkRDDWriteClient.upsert(SparkRDDWriteClient.java:155)
at org.apache.hudi.DataSourceUtils.doWriteOperation(DataSourceUtils.java:213)
at org.apache.hudi.HoodieSparkSqlWriter$.write(HoodieSparkSqlWriter.scala:306)
at org.apache.hudi.DefaultSource.createRelation(DefaultSource.scala:171)
at org.apache.spark.sql.execution.datasources.DataSource.writeAndRead(DataSource.scala:519)
at org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.saveDataIntoTable(createDataSourceTables.scala:228)
at org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.run(createDataSourceTables.scala:182)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:113)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:111)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.executeCollect(commands.scala:125)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.$anonfun$applyOrElse$1(QueryExecution.scala:115)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107)
at org.apache.spark.sql.execution.SQLExecution$.withTracker(SQLExecution.scala:232)
at org.apache.spark.sql.execution.SQLExecution$.executeQuery$1(SQLExecution.scala:110)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:135)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107)
at org.apache.spark.sql.execution.SQLExecution$.withTracker(SQLExecution.scala:232)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:135)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:253)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:134)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:68)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:112)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:108)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:519)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:83)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:519)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:495)
at org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:108)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:95)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:93)
at org.apache.spark.sql.Dataset.(Dataset.scala:221)
at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:101)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:98)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:618)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:613)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.lang.Thread.run(Thread.java:750)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 7 in stage 18.0 failed 4 times, most recent failure: Lost task 7.3 in stage 18.0 (TID 270) ([2a05:d018:116b:f601:5602:379f:9471:369e] executor 2): org.apache.avro.AvroRuntimeException: Not a valid schema field: ts
at org.apache.avro.generic.GenericData$Record.get(GenericData.java:256)
at org.apache.hudi.avro.HoodieAvroUtils.getNestedFieldVal(HoodieAvroUtils.java:501)
at org.apache.hudi.HoodieSparkSqlWriter$.$anonfun$write$10(HoodieSparkSqlWriter.scala:272)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:461)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:461)
at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:199)
at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:65)
at org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)
at org.apache.spark.scheduler.Task.run(Task.scala:133)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1474)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:750)

Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2610)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2559)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2558)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2558)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1200)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1200)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1200)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2798)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2740)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2729)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:978)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2215)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2236)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2255)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2280)
at org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1030)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:414)
at org.apache.spark.rdd.RDD.collect(RDD.scala:1029)
at org.apache.spark.api.java.JavaRDDLike.collect(JavaRDDLike.scala:362)
at org.apache.spark.api.java.JavaRDDLike.collect$(JavaRDDLike.scala:361)
at org.apache.spark.api.java.AbstractJavaRDDLike.collect(JavaRDDLike.scala:45)
at org.apache.hudi.data.HoodieJavaRDD.collectAsList(HoodieJavaRDD.java:157)
at org.apache.hudi.index.simple.HoodieSimpleIndex.fetchRecordLocationsForAffectedPartitions(HoodieSimpleIndex.java:142)
at org.apache.hudi.index.simple.HoodieSimpleIndex.tagLocationInternal(HoodieSimpleIndex.java:113)
at org.apache.hudi.index.simple.HoodieSimpleIndex.tagLocation(HoodieSimpleIndex.java:91)
at org.apache.hudi.table.action.commit.HoodieWriteHelper.tag(HoodieWriteHelper.java:49)
at org.apache.hudi.table.action.commit.HoodieWriteHelper.tag(HoodieWriteHelper.java:32)
at org.apache.hudi.table.action.commit.BaseWriteHelper.write(BaseWriteHelper.java:53)
... 58 more
Caused by: org.apache.avro.AvroRuntimeException: Not a valid schema field: ts
at org.apache.avro.generic.GenericData$Record.get(GenericData.java:256)
at org.apache.hudi.avro.HoodieAvroUtils.getNestedFieldVal(HoodieAvroUtils.java:501)
at org.apache.hudi.HoodieSparkSqlWriter$.$anonfun$write$10(HoodieSparkSqlWriter.scala:272)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:461)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:461)
at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:199)
at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:65)
at org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)
at org.apache.spark.scheduler.Task.run(Task.scala:133)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1474)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
```