Encrypting only one field in nested field prevents reading of other fields in nested field without keys

Hi Team,

While exploring parquet encryption, it is found that, if a field in nested column is encrypted , and If I want to read this parquet directory from other applications which does not have encryption keys to decrypt it, I cannot read the remaining fields of the nested column without keys. 

Example 

`
```java

case class nestedItem(ic: Int = 0, sic : Double, pc: Int = 0)
case class SquareItem(int_column: Int, square_int_column : Double, partitionCol: Int, nestedCol :nestedItem)
`
```

In the case class `SquareItem` , `nestedCol` field is nested field and I want to encrypt a field `ic` within it. 
 
I also want the footer to be non encrypted , so that I can use the encrypted parquet file by legacy applications. 
 
Encryption is successful, however, when I query the parquet file using spark 3.3.0 without having any configuration for parquet encryption set up , I cannot non encrypted fields of `nestedCol` `sic`. I was expecting that only `nestedCol` `ic` field will not be querable.
 
 
Reproducer. 
Spark 3.3.0 Using Spark-shell 
Downloaded the file [parquet-hadoop-1.12.0-tests.jar](https://repo1.maven.org/maven2/org/apache/parquet/parquet-hadoop/1.12.0/parquet-hadoop-1.12.0-tests.jar) and added it to spark-jars folder
Code to create encrypted data. #  

 
```java

sc.hadoopConfiguration.set("parquet.crypto.factory.class" ,"org.apache.parquet.crypto.keytools.PropertiesDrivenCryptoFactory")

sc.hadoopConfiguration.set("parquet.encryption.kms.client.class" ,"org.apache.parquet.crypto.keytools.mocks.InMemoryKMS")

sc.hadoopConfiguration.set("parquet.encryption.key.list","key1a: BAECAwQFBgcICQoLDA0ODw==, key2a: BAECAAECAAECAAECAAECAA==, keyz: BAECAAECAAECAAECAAECAA==")

sc.hadoopConfiguration.set("parquet.encryption.key.material.store.internally","false")

val encryptedParquetPath = "/tmp/par_enc_footer_non_encrypted"
valpartitionCol = 1
case class nestedItem(ic: Int = 0, sic : Double, pc: Int = 0)
case class SquareItem(int_column: Int, square_int_column : Double, partitionCol: Int, nestedCol :nestedItem)
val dataRange = (1 to 100).toList
val squares = sc.parallelize(dataRange.map(i => new SquareItem(i, scala.math.pow(i,2), partitionCol,nestedItem(i,i))))
squares.toDS().show()
squares.toDS().write.partitionBy("partitionCol").mode("overwrite").option("parquet.encryption.column.keys", "key1a:square_int_column,nestedCol.ic;").option("parquet.encryption.plaintext.footer",true).option("parquet.encryption.footer.key", "keyz").parquet(encryptedParquetPath)
```

Code to read the data trying to access non encrypted nested field by opening a new spark-shell
 

```java

val encryptedParquetPath = "/tmp/par_enc_footer_non_encrypted"
spark.sqlContext.read.parquet(encryptedParquetPath).createOrReplaceTempView("test")
spark.sql("select nestedCol.sic from test").show()
```

As you can see that nestedCol.sic is not encrypted , I was expecting the results, but
I get the below error
 

```java

Caused by: org.apache.parquet.crypto.ParquetCryptoRuntimeException: [square_int_column]. Null File Decryptor
  at org.apache.parquet.hadoop.metadata.EncryptedColumnChunkMetaData.decryptIfNeeded(ColumnChunkMetaData.java:602)
  at org.apache.parquet.hadoop.metadata.ColumnChunkMetaData.getEncodings(ColumnChunkMetaData.java:348)
  at org.apache.parquet.hadoop.ParquetRecordReader.checkDeltaByteArrayProblem(ParquetRecordReader.java:191)
  at org.apache.parquet.hadoop.ParquetRecordReader.initializeInternalReader(ParquetRecordReader.java:177)
  at org.apache.parquet.hadoop.ParquetRecordReader.initialize(ParquetRecordReader.java:140)
  at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat.$anonfun$buildReaderWithPartitionValues$1(ParquetFileFormat.scala:375)
  at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.org$apache$spark$sql$execution$datasources$FileScanRDD$$anon$$readCurrentFile(FileScanRDD.scala:209)
  at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:270)
  at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:116)
  at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
  at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
  at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
  at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:760)
  at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:364)
  at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:890)
  at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:890)
  at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
  at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:365)
  at org.apache.spark.rdd.RDD.iterator(RDD.scala:329)
  at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
  at org.apache.spark.scheduler.Task.run(Task.scala:136)
  at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:548)
  at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1504)
  at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:551)
  at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
  at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
  at java.base/java.lang.Thread.run(Thread.java:833)
```

**Reporter**: [Vignesh Nageswaran](https://issues.apache.org/jira/secure/ViewProfile.jspa?name=Nageswaran)
#### Related issues:
- [Encrypted files should not be checked for delta encoding problem](https://github.com/apache/parquet-java/issues/2779) (is caused by)

_{**Note**: *This issue was originally created as [PARQUET-2193](https://issues.apache.org/jira/browse/PARQUET-2193). Please see the [migration documentation](https://issues.apache.org/jira/browse/PARQUET-2502) for further details.*}

Tái hiện trường hợp mã hóa cột lồng nhau với Spark 3.3.0 và JAR kiểm thử của parquet-hadoop 1.12.0, sau đó kiểm tra đường đi của stack trace qua ParquetRecordReader và ColumnChunkMetaData. Kết quả mong muốn là việc chọn nestedCol.sic không được mã hóa sẽ thành công mà không cần khóa cho nestedCol.ic hoặc square_int_column được mã hóa; hãy so sánh hành vi này với issue liên quan #2779.

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