`integer overflow` in CapacityByteArrayOutputStream

### Describe the bug, including details regarding any error messages, version, and platform.

The following exception was thrown when we read a column of `ARRAY` in Spark 3.5.0 and Parquet 1.15.2

```
Caused by: java.lang.ArithmeticException: integer overflow
at java.base/java.lang.Math.addExact(Math.java:883)
at org.apache.parquet.bytes.CapacityByteArrayOutputStream.addSlab(CapacityByteArrayOutputStream.java:198)
at org.apache.parquet.bytes.CapacityByteArrayOutputStream.write(CapacityByteArrayOutputStream.java:220)
at org.apache.parquet.bytes.LittleEndianDataOutputStream.write(LittleEndianDataOutputStream.java:76)
at java.base/java.io.OutputStream.write(OutputStream.java:127)
at org.apache.parquet.io.api.Binary$ByteArrayBackedBinary.writeTo(Binary.java:319)
at org.apache.parquet.column.values.plain.PlainValuesWriter.writeBytes(PlainValuesWriter.java:55)
at org.apache.parquet.column.values.fallback.FallbackValuesWriter.writeBytes(FallbackValuesWriter.java:178)
at org.apache.parquet.column.impl.ColumnWriterBase.write(ColumnWriterBase.java:196)
at org.apache.parquet.io.MessageColumnIO$MessageColumnIORecordConsumer.addBinary(MessageColumnIO.java:473)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeWriter$9(ParquetWriteSupport.scala:212)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeWriter$9$adapted(ParquetWriteSupport.scala:210)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeArrayWriter$5(ParquetWriteSupport.scala:354)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.consumeField(ParquetWriteSupport.scala:490)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeArrayWriter$4(ParquetWriteSupport.scala:354)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.consumeGroup(ParquetWriteSupport.scala:484)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeArrayWriter$3(ParquetWriteSupport.scala:352)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.consumeField(ParquetWriteSupport.scala:490)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeArrayWriter$2(ParquetWriteSupport.scala:347)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.consumeGroup(ParquetWriteSupport.scala:484)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeArrayWriter$1(ParquetWriteSupport.scala:346)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeArrayWriter$1$adapted(ParquetWriteSupport.scala:342)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$writeFields$1(ParquetWriteSupport.scala:168)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.consumeField(ParquetWriteSupport.scala:490)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.writeFields(ParquetWriteSupport.scala:168)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$write$1(ParquetWriteSupport.scala:158)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.consumeMessage(ParquetWriteSupport.scala:478)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.write(ParquetWriteSupport.scala:158)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.write(ParquetWriteSupport.scala:54)
at org.apache.parquet.hadoop.InternalParquetRecordWriter.write(InternalParquetRecordWriter.java:152)
at org.apache.parquet.hadoop.ParquetRecordWriter.write(ParquetRecordWriter.java:240)
at org.apache.parquet.hadoop.ParquetRecordWriter.write(ParquetRecordWriter.java:41)
at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.write(ParquetOutputWriter.scala:39)
```

The issue can be worked around by increasing `spark.sql.shuffle.partitions` to divide data into smaller partitions.
Can it be solved at parquet side?

### Component(s)

Core

Commencez par CapacityByteArrayOutputStream.java, en particulier addSlab à la ligne 198 et write à la ligne 220, puis reproduisez le scénario d’écriture ARRAY signalé avec Spark 3.5.0 et Parquet 1.15.2. Suivez le chemin qui mène le calcul de capacité jusqu’à Math.addExact et confirmez que l’échec dû au dépassement d’entier est évité sans perturber les écritures volumineuses.

Rédigé par le modèle d'indexation à partir du texte de l'issue.