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

Bắt đầu với reproducer được cung cấp và lệnh gọi DirectCodecFactory.createDirectCodecFactory, sau đó so sánh hành vi của nó với codec factory của Hadoop được ParquetReader sử dụng. Sử dụng tệp test.parquet đính kèm và xác minh rằng việc đọc hoàn tất với 51.000 bản ghi và giữ nguyên nội dung key/value ожида kiến.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.