[SUPPORT] Duplicated partitions rows in MDT when reading w/ datasource

hudi >= 0.11 (including 0.13.1)

I noticed we have duplicates in our metadata tables :
```
>>> spark.read.format("hudi").load("/tmp/metadata").filter("key='version=2/event_date=2009-12-03/event_hour=08'").select("key","filesystemMetadata").show(10, False,True)
-RECORD 0-------------------------------------------------------------------------------------------------------------------
key | version=2/event_date=2009-12-03/event_hour=08
filesystemMetadata | {3f5fc9bd-2d4e-4c0a-8b9b-f29dbcefc579-0_14818-18-113282_20220728200748277.parquet -> {445028, false}}
-RECORD 1-------------------------------------------------------------------------------------------------------------------
key | version=2/event_date=2009-12-03/event_hour=08
filesystemMetadata | {3f5fc9bd-2d4e-4c0a-8b9b-f29dbcefc579-0_14818-18-113282_20220728200748277.parquet -> {445028, false}}
-RECORD 2-------------------------------------------------------------------------------------------------------------------
key | version=2/event_date=2009-12-03/event_hour=08
filesystemMetadata | {3f5fc9bd-2d4e-4c0a-8b9b-f29dbcefc579-0_14818-18-113282_20220728200748277.parquet -> {445028, false}}
-RECORD 3-------------------------------------------------------------------------------------------------------------------
key | version=2/event_date=2009-12-03/event_hour=08
filesystemMetadata | {3f5fc9bd-2d4e-4c0a-8b9b-f29dbcefc579-0_14818-18-113282_20220728200748277.parquet -> {445028, false}}
-RECORD 4-------------------------------------------------------------------------------------------------------------------
key | version=2/event_date=2009-12-03/event_hour=08
filesystemMetadata | {3f5fc9bd-2d4e-4c0a-8b9b-f29dbcefc579-0_14818-18-113282_20220728200748277.parquet -> {445028, false}}
-RECORD 5-------------------------------------------------------------------------------------------------------------------
key | version=2/event_date=2009-12-03/event_hour=08
filesystemMetadata | {3f5fc9bd-2d4e-4c0a-8b9b-f29dbcefc579-0_14818-18-113282_20220728200748277.parquet -> {445028, false}}
-RECORD 6-------------------------------------------------------------------------------------------------------------------
key | version=2/event_date=2009-12-03/event_hour=08
filesystemMetadata | {3f5fc9bd-2d4e-4c0a-8b9b-f29dbcefc579-0_14818-18-113282_20220728200748277.parquet -> {445028, false}}
-RECORD 7-------------------------------------------------------------------------------------------------------------------
key | version=2/event_date=2009-12-03/event_hour=08
filesystemMetadata | {3f5fc9bd-2d4e-4c0a-8b9b-f29dbcefc579-0_14818-18-113282_20220728200748277.parquet -> {445028, false}}
```

Interestingly, I also have 1 hfile and 7 logs. On our other tables, the number of duplicated partitions is equal to the number of logs files.
```
ls /tmp/metadata/files/
.hoodie_partition_metadata
files-0000_0-16-519_20230620071307473001.hfile
.files-0000_20230620071307473001.log.1_0-23-725
.files-0000_20230620071307473001.log.2_0-16-719
.files-0000_20230620071307473001.log.3_0-16-721
.files-0000_20230620071307473001.log.4_0-16-723
.files-0000_20230620071307473001.log.5_0-16-724
.files-0000_20230620071307473001.log.6_0-16-727
.files-0000_20230620071307473001.log.7_0-16-729
```

Here is a reproductible script. after reaching the compaction number, the mdt sudently gets duplicates when reading:

```python
tableName = 'test_corrupted_mdt'
basePath = "/tmp/{tableName}".format(tableName=tableName)

hudi_options = {
"hoodie.table.name": tableName,
"hoodie.datasource.write.recordkey.field": "event_id",
"hoodie.datasource.write.partitionpath.field": "version,event_date",
"hoodie.datasource.write.table.name": tableName,
"hoodie.datasource.write.operation": "upsert",
"hoodie.datasource.write.precombine.field": "ts",
"hoodie.datasource.write.keygenerator.class": "org.apache.hudi.keygen.ComplexKeyGenerator",
"hoodie.datasource.write.hive_style_partitioning": "true",
"hoodie.datasource.hive_sync.enable": "false",
"hoodie.metadata.enable": "true",
}
mode="overwrite"
for i in range(1,11):
df =spark.sql("select '1' as event_id, '2' as ts, '"+str(i)+"' as version, 'foo' as event_date")
(df.write.format("hudi").options(**hudi_options).mode(mode).save(basePath))
mode="append"

spark.read.format("hudi").load(basePath + "/.hoodie/metadata").count()
spark.read.format("hudi").load(basePath + "/.hoodie/metadata").select("key").dropDuplicates().count()

>>> spark.read.format("hudi").load(basePath + "/.hoodie/metadata").count()
21
>>> spark.read.format("hudi").load(basePath + "/.hoodie/metadata").select("key").dropDuplicates().count()
11

```