Enhance UUID Compatibility in Iceberg to Hudi Conversion

### Feature Request / Improvement

# Context
In #112 , we are working on adding UUID support in XTable. Currently, only Iceberg has the concept of a UUID, which is stored as a fixed-size byte array in the underlying Parquet files. To facilitate conversion between Iceberg and formats like Delta or Hudi, we introduced a UUID internal type.

# Problem Statement
While converting UUIDs from Iceberg to Hudi, we encountered an issue with Spark’s ParquetSchemaConverter. It appears that Spark cannot read FIXED_LEN_BYTE_ARRAY fields annotated with the UUID logical type in Parquet. You can refer to the relevant source code here: [ParquetSchemaConverter](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaConverter.scala#L299-L305).

As of now, we have successfully implemented UUID conversion for Iceberg to Delta. However, Iceberg to Hudi remains a limitation due to this issue, which we hope to address in the future.

# Error Log
```

Caused by: org.apache.spark.sql.AnalysisException: Illegal Parquet type: FIXED_LEN_BYTE_ARRAY (UUID).
at org.apache.spark.sql.errors.QueryCompilationErrors$.illegalParquetTypeError(QueryCompilationErrors.scala:1762)
at org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.illegalType$1(ParquetSchemaConverter.scala:206)
at org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.$anonfun$convertPrimitiveField$2(ParquetSchemaConverter.scala:310)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.convertPrimitiveField(ParquetSchemaConverter.scala:224)
at org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.convertField(ParquetSchemaConverter.scala:187)
at org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.$anonfun$convertInternal$3(ParquetSchemaConverter.scala:147)
at org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.$anonfun$convertInternal$3$adapted(ParquetSchemaConverter.scala:117)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
at scala.collection.immutable.Range.foreach(Range.scala:158)
at scala.collection.TraversableLike.map(TraversableLike.scala:286)
at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
at scala.collection.AbstractTraversable.map(Traversable.scala:108)
at org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.convertInternal(ParquetSchemaConverter.scala:117)
at org.apache.spark.sql.execution.datasources.parquet.ParquetToSparkSchemaConverter.convert(ParquetSchemaConverter.scala:87)
at org.apache.spark.sql.execution.datasources.parquet.HoodieParquetFileFormatHelper$.buildImplicitSchemaChangeInfo(HoodieParquetFileFormatHelper.scala:31)
at org.apache.spark.sql.execution.datasources.parquet.Spark34LegacyHoodieParquetFileFormat.$anonfun$buildReaderWithPartitionValues$2(Spark34LegacyHoodieParquetFileFormat.scala:254)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.org$apache$spark$sql$execution$datasources$FileScanRDD$$anon$$readCurrentFile(FileScanRDD.scala:231)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:293)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:125)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:140)
at org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:101)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
at java.base/java.lang.Thread.run(Thread.java:829)
```

### Are you willing to submit PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://www.apache.org/foundation/policies/conduct)

Start with Spark's ParquetSchemaConverter at the linked source location and examine how it handles FIXED_LEN_BYTE_ARRAY fields annotated with UUID. Then trace XTable's Iceberg-to-Hudi conversion path; done means Hudi can read converted UUID data without the reported AnalysisException, with the existing Iceberg-to-Delta support preserved.

Written by the indexing model from the issue text.