Support LZO Compression for Parquet Tables in Blaze

**Describe the bug**

Blaze does not support the LZO compression format for Parquet tables.

**To Reproduce**
Steps to reproduce the behavior:
1. Create an LZO-compressed Parquet table:
```
CREATE TABLE IF NOT EXISTS people_35 (id INT, name STRING, age INT) USING parquet OPTIONS (compression 'lzo');
```
2. Insert data into the test table:
```
INSERT INTO people_35 VALUES (1, 'Alice', 25), (2, 'Bob', 30), (3, 'Charlie', 35), (4, 'David', 40), (5, 'Eve', 45);
```
3. Query the test table with Blaze enabled/disabled:
```
set spark.blaze.enable=true/false;
> select * from people_35;
```
When attempting to query the LZO-compressed Parquet table with Blaze enabled, the following error message is encountered:
```
spark-sql (blaze_db)> 25/01/17 17:17:02 WARN TaskSetManager: Lost task 3.3 in stage 0.0 (TID 18) : TaskKilled (Stage cancelled: Job aborted due to stage failure: Task 4 in stage 0.0 failed 4 times, most recent failure: Lost task 4.3 in stage 0.0 (TID 17) (executor 4): java.lang.RuntimeException: poll record batch error: Execution error: native execution panics: Execution error: Execution error: output_with_sender[ParquetScan] error: Execution error: output_with_sender[ParquetScan]: output() returns error: Arrow error: External error: NYI: The codec type LZO is not supported yet
at org.apache.spark.sql.blaze.JniBridge.nextBatch(Native Method)
at org.apache.spark.sql.blaze.BlazeCallNativeWrapper$$anon$1.hasNext(BlazeCallNativeWrapper.scala:80)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:388)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:893)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:893)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:367)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:331)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:95)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:166)
at org.apache.spark.scheduler.Task.run(Task.scala:143)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$5(Executor.scala:662)
at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64)
at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:95)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:682)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
```

**Screenshots**

**Additional context**
Spark Version: 3.5