Reading fails when using DirectCodecFactory

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

Hello folks,

I'm currently working on removing the `hadoop-common` dependency from the runtime in one of my projects.

As part of this process, I need to replace the "Hadoop" codec factory, which relies on certain classes from `hadoop-common`, with a codec factory that exclusively uses classes from `parquet-hadoop`. One potential option is `DirectCodecFactory`, but it comes with a problem.

For demonstration purposes, I’m using a simple key-value Snappy-compressed parquet file containing 51,000 records. Here’s a sample of the data:

When attempting to read this file using `DirectCodecFactory`, I encounter two issues:
1. The file fails to read completely. Near the end, it throws an error: `Can't read value in column [key] optional binary key (STRING) = 0 at value 49,534 out of 51,000, 9,534 out of 11,000 in currentPage. Repetition level: 0, definition level: 1.`
2. At record number 40,001, the key and value columns get mixed up, with the key column unexpectedly containing a value.

Parquet version: 1.15.0
Hadoop version: 3.4.1

Observations:
1. With the Hadoop codec factory the file can be read without any issues.

Here are tests demonstrating the issues:
```java
package sandbox.parquet.reader;

import org.apache.hadoop.conf.Configuration;
import org.apache.parquet.bytes.DirectByteBufferAllocator;
import org.apache.parquet.column.ParquetProperties;
import org.apache.parquet.conf.ParquetConfiguration;
import org.apache.parquet.conf.PlainParquetConfiguration;
import org.apache.parquet.hadoop.CodecFactory;
import org.apache.parquet.hadoop.ParquetReader;
import org.apache.parquet.hadoop.api.InitContext;
import org.apache.parquet.hadoop.api.ReadSupport;
import org.apache.parquet.io.InputFile;
import org.apache.parquet.io.LocalInputFile;
import org.apache.parquet.io.api.Binary;
import org.apache.parquet.io.api.Converter;
import org.apache.parquet.io.api.GroupConverter;
import org.apache.parquet.io.api.PrimitiveConverter;
import org.apache.parquet.io.api.RecordMaterializer;
import org.apache.parquet.schema.GroupType;
import org.apache.parquet.schema.MessageType;
import org.apache.parquet.schema.Type;
import org.junit.jupiter.api.Assertions;
import org.junit.jupiter.api.Test;

import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.Map;

public class ParquetReaderTest {

@Test
void readParquetFileAndVerifyRecordCount_usingDirectCodeFactory() throws Exception {
Path filePath = Paths.get(ClassLoader.getSystemResource("test.parquet").toURI());
int recordCount = 0;
try(ParquetReader parquetReader = createReaderWithDirectCodeFactory(filePath)) {
while (parquetReader.read() != null) {
recordCount++;
}
}

Assertions.assertEquals(51_000, recordCount);
}

@Test
void readParquetFileAndVerifyContent_usingDirectCodeFactory() throws Exception {
Path filePath = Paths.get(ClassLoader.getSystemResource("test.parquet").toURI());
int recordCount = 0;
try(ParquetReader parquetReader = createReaderWithHadoopCodeFactory(filePath)) {
String[] record;
while ((record = parquetReader.read()) != null) {
Assertions.assertEquals("key_" + (recordCount + 1), record[0]);
Assertions.assertEquals("value_" + (recordCount + 1), record[1]);
recordCount++;
}
}

Assertions.assertEquals(51_000, recordCount);
}

@Test
void readParquetFileAndVerifyRecordCount_usingHadoopCodeFactory() throws Exception {
Path filePath = Paths.get(ClassLoader.getSystemResource("test.parquet").toURI());
int recordCount = 0;
try(ParquetReader parquetReader = createReaderWithHadoopCodeFactory(filePath)) {
while (parquetReader.read() != null) {
recordCount++;
}
}

Assertions.assertEquals(51_000, recordCount);
}

@Test
void readParquetFileAndVerifyContent_usingHadoopCodeFactory() throws Exception {
Path filePath = Paths.get(ClassLoader.getSystemResource("test.parquet").toURI());
int recordCount = 0;
try(ParquetReader parquetReader = createReaderWithDirectCodeFactory(filePath)) {
String[] record;
while ((record = parquetReader.read()) != null) {
Assertions.assertEquals("key_" + (recordCount + 1), record[0]);
Assertions.assertEquals("value_" + (recordCount + 1), record[1]);
recordCount++;
}
}

Assertions.assertEquals(51_000, recordCount);
}

private ParquetReader createReaderWithDirectCodeFactory(Path file) throws Exception {
return new ParquetReaderBuilder(new LocalInputFile(file), new PlainParquetConfiguration())
.withCodecFactory(CodecFactory.createDirectCodecFactory(null,
DirectByteBufferAllocator.getInstance(),
ParquetProperties.DEFAULT_PAGE_SIZE))
.build();
}

private ParquetReader createReaderWithHadoopCodeFactory(Path file) throws Exception {
// the Hadoop codec factory is created in ParquetReadOptions.Builder#build
return new ParquetReaderBuilder(new LocalInputFile(file), new PlainParquetConfiguration())
.build();
}

static class ParquetReaderBuilder extends ParquetReader.Builder {

ParquetReaderBuilder(InputFile file, ParquetConfiguration conf) {
super(file, conf);
}

@Override
protected ReadSupport getReadSupport() {
return new TestReadSupport();
}
}

static class TestReadSupport extends ReadSupport {

@Override
public ReadContext init(InitContext context) {
return new ReadContext(context.getFileSchema());
}

@Override
public RecordMaterializer prepareForRead(Configuration configuration,
Map keyValueMetaData,
MessageType fileSchema,
ReadContext readContext) {
return new TestRecordMaterializer(fileSchema);
}

@Override
public RecordMaterializer prepareForRead(ParquetConfiguration configuration,
Map keyValueMetaData,
MessageType fileSchema,
ReadContext readContext) {
return new TestRecordMaterializer(fileSchema);
}
}

static class TestRecordMaterializer extends RecordMaterializer {

private final TestRootGroupConverter root;

TestRecordMaterializer(MessageType schema) {
this.root = new TestRootGroupConverter(schema);
}

@Override
public String[] getCurrentRecord() {
return root.getCurrentRecord();
}

@Override
public GroupConverter getRootConverter() {
return root;
}
}

static class TestRootGroupConverter extends GroupConverter {
private String[] currentRecord;
private final Converter[] converters;

TestRootGroupConverter(GroupType schema) {
converters = new Converter[schema.getFieldCount()];

for (int i = 0; i < converters.length; i++) {
final Type type = schema.getType(i);
if (type.isPrimitive()) {
converters[i] = new TestPrimitiveConverter(this, i);
} else {
throw new RuntimeException("Nested records not supported!");
}
}
}

@Override
public Converter getConverter(int fieldIndex) {
return converters[fieldIndex];
}

@Override
public void start() {
currentRecord = new String[converters.length];
}

@Override
public void end() {
}

String[] getCurrentRecord() {
return currentRecord;
}
}

static class TestPrimitiveConverter extends PrimitiveConverter {

private final TestRootGroupConverter parent;
private final int index;

TestPrimitiveConverter(TestRootGroupConverter parent, int index) {
this.parent = parent;
this.index = index;
}

@Override
public void addBinary(Binary value) {
parent.getCurrentRecord()[index] = value.toStringUsingUTF8();
}

@Override
public void addBoolean(boolean value) {
throw new UnsupportedOperationException();
}

@Override
public void addDouble(double value) {
throw new UnsupportedOperationException();
}

@Override
public void addFloat(float value) {
throw new UnsupportedOperationException();
}

@Override
public void addInt(int value) {
throw new UnsupportedOperationException();
}

@Override
public void addLong(long value) {
throw new UnsupportedOperationException();
}
}
}
```

Attaching the parquet file and a demo application:
* [test.parquet.zip](https://github.com/user-attachments/files/18718126/test.parquet.zip)
* [parquet-reader.zip](https://github.com/user-attachments/files/18718132/parquet-reader.zip)

### Component(s)

Core

Beginne mit dem bereitgestellten Reproducer und dem Aufruf von DirectCodecFactory.createDirectCodecFactory und vergleiche anschließend sein Verhalten mit der von ParquetReader verwendeten Hadoop-Codec-Fabrik. Verwende die angehängte Datei test.parquet und überprüfe, dass das Lesen mit 51.000 Datensätzen abgeschlossen wird und der erwartete Schlüssel-/Wertinhalt erhalten bleibt.

Vom Indexierungsmodell aus dem Issue-Text verfasst.