[Java] Memory leak when use VectorSchemaSlice slice

### Describe the usage question you have. Please include as many useful details as possible.

In my Flink process function, I receive serialized VectorSchemaRoot data which needs to be deserialized for further processing. As I need to operate on it row by row, I utilized the slice function. However, this approach can lead to an increase in direct memory usage which in turn reduces the heap memory space in Flink. Ultimately, this can result in an Out Of Memory (OOM) exception as the heap memory space becomes insufficient. However, if I serialize the sliced VectorSchemaRoot and pass it as a parameter to the downstream function, which then deserializes it again, there will be no more OOM issues.

```
public void processElement(I value, ProcessFunction.Context context, Collector collector) throws Exception {
if (this.writerHelper == null) {
initWriterHelper();
}
reader = new ArrowStreamReader(new ByteArrayInputStream((byte[]) value), ArrowUtil.rootAllocator);
try {
while (reader.loadNextBatch()) {
VectorSchemaRoot vsr = reader.getVectorSchemaRoot();
int rowCount = vsr.getRowCount();
for (int i = 0; i < rowCount; i++) {
//split to row
VectorSchemaRoot row = vsr.slice(i, 1);
// this.writerHelper.write(row); This approach can result in a memory leak, whereas the following method will not.

ByteArrayOutputStream out = new ByteArrayOutputStream();
ArrowStreamWriter writer =
new ArrowStreamWriter(row, null, Channels.newChannel(out));
writer.start();
writer.writeBatch();
this.writerHelper.write(out.toByteArray());
row.clear();
row.close();
}
vsr.clear();
vsr.close();
}
} catch (Exception ex) {
ex.printStackTrace();
} finally {
reader.close();
}
}```

### Component(s)

Java

Start with the processElement example, especially VectorSchemaRoot.slice(i, 1) and the row.clear()/row.close() calls. Reproduce the direct-memory growth while writing sliced rows through writerHelper, then compare it with the serialize-and-deserialize path. Done means the sliced-row path no longer causes the reported memory growth or OOM.

Written by the indexing model from the issue text.