[Java][Python] Avoid Reuse VectorSchemaRoot for exporting ArrowArrayStream to other language

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

I'm trying to import/export data to database in python through `ArrayStream` over pyarrow.jvm and JDBC.

In order to export ArrowVectorIterator as stream without unloading to RecordBatch on java side before it export to stream, I wrap ArrowVectorIterator into ArrowReader as below:
```java
public class ArrowVectorIteratorReader extends ArrowReader {

private final Iterator iterator;
private final Schema schema;
private VectorSchemaRoot root;

public ArrowVectorIteratorReader(BufferAllocator allocator, Iterator iterator, Schema schema) {
super(allocator);
this.iterator = iterator;
this.schema = schema;
this.root = null;
}

@Override
public VectorSchemaRoot getVectorSchemaRoot() throws IOException {
if (root == null) return super.getVectorSchemaRoot();
return root;
}

@Override
public boolean loadNextBatch() throws IOException {
if (iterator.hasNext()) {
VectorSchemaRoot lastRoot = root;
root = iterator.next();
if (root != lastRoot && lastRoot != null) lastRoot.close();
return true;
} else {
return false;
}
}

@Override
public long bytesRead() {
return 0;
}

@Override
protected void closeReadSource() throws IOException {
if (iterator instanceof AutoCloseable) {
try {
((AutoCloseable) iterator).close();
} catch (Exception e) {
throw new IOException(e);
}
}
root.close();
}

@Override
protected Schema readSchema() throws IOException {
return schema;
}
}

```

When ArrowVectorIterator use the config with `reuseVectorSchemaRoot` is enabled, utf8 array may crushed on python side, but works as expectred on java side.

Java code as below
```java
try (final ArrowReader source = porter.importData(1); returns ArrowVectorIteratorReader with batchSize=1
final ArrowArrayStream stream = ArrowArrayStream.allocateNew(allocator)) {
Data.exportArrayStream(allocator, source, stream);

try (final ArrowReader reader = Data.importArrayStream(allocator, stream)) {
while (reader.loadNextBatch()) {
// root from getVectorSchemaRoot() is legal on every vector
totalRecord += reader.getVectorSchemaRoot().getRowCount();
}
}
}
```

On Python side, the situation is unexplainable.
The exported stream from Java in wrapped into a RecordBatchReader and write into different file formats.
```python
def wrap_from_java_stream_to_generator(java_arrow_stream, allocator=None, yield_schema=False):
if allocator is None:
allocator = get_java_root_allocator().allocator
c_stream = arrow_c.new("struct ArrowArrayStream*")
c_stream_ptr = int(arrow_c.cast("uintptr_t", c_stream))

org = jpype. JPackage("org")
java_wrapped_stream = org.apache.arrow.c.ArrowArrayStream.wrap(c_stream_ptr)

org.apache.arrow.c.Data.exportArrayStream(allocator, java_arrow_stream, java_wrapped_stream)

# noinspection PyProtectedMember
with pa. RecordBatchReader._import_from_c(c_stream_ptr) as reader: # type: pa. RecordBatchReader
if yield_schema:
yield reader.schema
yield from reader

def wrap_from_java_stream(java_arrow_stream, allocator=None):
generator = wrap_from_java_stream_to_generator(java_arrow_stream, allocator, yield_schema=True)
schema = next(generator)

return pa. RecordBatchReader.from_batches(schema, generator)
```
For CSV, works as expected
```python
with wrap_from_java_stream(java_arrow_stream, allocator) as stream:
with pa.csv.CSVWriter(csv_path, stream.schema) as writer:
for record_batch in stream:
writer.write_batch(record_batch)
```

For Parquet, writing with dataset api as below
```python
with wrap_from_java_stream(java_arrow_stream, allocator) as stream:
pa.dataset.write_dataset(stream, data_path, format="parquet")
```

```
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
......./python3.8/site-packages/pyarrow/dataset.py:999: in write_dataset
_filesystemdataset_write(
pyarrow/_dataset.pyx:3655: in pyarrow._dataset._filesystemdataset_write
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

> ???
E pyarrow.lib.ArrowInvalid: Parquet cannot store strings with size 2GB or more
```
OR
```
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

> ???
E pyarrow.lib.ArrowInvalid: Column 1: In chunk 0: Invalid: Length spanned by binary offsets (7) larger than values array (size 6)
```

In order to making out which record raises error, RecordBatchReader is wrapped into a smaller batch size and log the content as below:
```python
with wrap_from_java_stream(java_arrow_stream, allocator) as stream:
def generator():
for rb in stream:
for i in range(rb.num_rows):
slice = rb.slice(i,1)
logger.info(slice.to_pylist())
yield slice
pa.dataset.write_dataset(pa.RecordBatchReader.from_batches(stream.schema, generator(), data_path, format="parquet")
```
Although the logger can print the slice, but write_dataset fails
```
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
......./python3.8/site-packages/pyarrow/dataset.py:999: in write_dataset
_filesystemdataset_write(
pyarrow/_dataset.pyx:3655: in pyarrow._dataset._filesystemdataset_write
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

> ???
E pyarrow.lib.ArrowInvalid: Column 1: In chunk 0: Invalid: First or last binary offset out of bounds
```

For arrow/feather format, it seems directly write record_batch into files, but when record_batch is invalid when reading from file (code is similar as above)

Then, if I create the ArrowVectorIteratorReader without reuseVectorSchemaRoot, everything works fine on Python side.

### Component(s)

Java, Python

首先，使用 issue 中展示的自定义 ArrowReader 实现跟踪 Data.exportArrayStream，重点关注 loadNextBatch()、getVectorSchemaRoot() 以及 reuseVectorSchemaRoot 的处理。使用 pyarrow 和 parquet write_dataset 复现 Java-to-Python 流；当复用的 VectorSchemaRoot 批次在整个导出流中保持有效且 Python 写入成功时，即表示完成。

由索引模型根据 Issue 内容生成。