Unable to save the complete pipeline to disk

**Describe the bug**
I'm trying to write the complete ML pipeline including StringIndexer, VectorAssembler and LightGBMRegressor to the disk using pipeline_model.write().overwrite().save("model_file"), but I'm unable to write to disk.

**To Reproduce**
```
from pyspark.sql import SparkSession
from pyspark.ml.feature import StringIndexer
from pyspark.ml.feature import VectorAssembler
from synapse.ml.lightgbm import LightGBMRegressor
from pyspark.ml import Pipeline, PipelineModel

spark = SparkSession.builder.appName('Regression') \
.config("spark.jars.packages", "com.microsoft.azure:synapseml_2.12:0.9.4") \
.config("spark.jars.repositories", "https://mmlspark.azureedge.net/maven") \
.getOrCreate()

df = spark.read.option('header', 'true').csv('tips.csv',inferSchema=True)
indexer = StringIndexer(inputCols=["sex", "smoker", "day", "time"],outputCols=["sex_indexed", "smoker_indexed", "day_indexed", "time_indexed"])
featureAssembler = VectorAssembler(inputCols=['tip', 'size', 'sex_indexed', 'smoker_indexed', 'day_indexed', 'time_indexed'], outputCol='Independent Features')

reggressor = LightGBMRegressor() \
.setLabelCol('total_bill') \
.setFeaturesCol('Independent Features') \
.setNumIterations(10) \
.setNumLeaves(10) \
.setMaxDepth(10) \
.setLearningRate(0.1) \
.setVerbosity(-1) \
.setBaggingSeed(100)

pipeline = Pipeline(stages=[indexer, featureAssembler, reggressor])
model = pipeline.fit(df)

model.write().overwrite().save("model_file")
```

**Expected behavior**
A clear and concise description of what you expected to happen.

**Info (please complete the following information):**
- SynapseML Version: 0.9.4
- Spark Version: pyspark 3.2.0
- Spark Platform [e.g. Databricks]: standalone mode

** Stacktrace**
```
Py4JJavaError: An error occurred while calling o417.save.
: java.lang.NoSuchMethodError: org.json4s.JsonDSL$.pair2Assoc(Lscala/Tuple2;Lscala/Function1;)Lorg/json4s/JsonDSL$JsonAssoc;
at org.apache.spark.ml.ComplexParamsWriter$.getMetadataToSave(ComplexParamsSerializer.scala:122)
at org.apache.spark.ml.ComplexParamsWriter$.saveMetadata(ComplexParamsSerializer.scala:97)
at org.apache.spark.ml.ComplexParamsWriter.saveImpl(ComplexParamsSerializer.scala:40)
at org.apache.spark.ml.util.MLWriter.save(ReadWrite.scala:168)
at org.apache.spark.ml.Pipeline$SharedReadWrite$.$anonfun$saveImpl$5(Pipeline.scala:257)
at org.apache.spark.ml.MLEvents.withSaveInstanceEvent(events.scala:174)
at org.apache.spark.ml.MLEvents.withSaveInstanceEvent$(events.scala:169)
at org.apache.spark.ml.util.Instrumentation.withSaveInstanceEvent(Instrumentation.scala:42)
at org.apache.spark.ml.Pipeline$SharedReadWrite$.$anonfun$saveImpl$4(Pipeline.scala:257)
at org.apache.spark.ml.Pipeline$SharedReadWrite$.$anonfun$saveImpl$4$adapted(Pipeline.scala:254)
at scala.collection.IndexedSeqOptimized.foreach(IndexedSeqOptimized.scala:36)
at scala.collection.IndexedSeqOptimized.foreach$(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:198)
at org.apache.spark.ml.Pipeline$SharedReadWrite$.$anonfun$saveImpl$1(Pipeline.scala:254)
at org.apache.spark.ml.Pipeline$SharedReadWrite$.$anonfun$saveImpl$1$adapted(Pipeline.scala:247)
at org.apache.spark.ml.util.Instrumentation$.$anonfun$instrumented$1(Instrumentation.scala:191)
at scala.util.Try$.apply(Try.scala:213)
at org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:191)
at org.apache.spark.ml.Pipeline$SharedReadWrite$.saveImpl(Pipeline.scala:247)
at org.apache.spark.ml.PipelineModel$PipelineModelWriter.saveImpl(Pipeline.scala:346)
at org.apache.spark.ml.util.MLWriter.save(ReadWrite.scala:168)
at org.apache.spark.ml.PipelineModel$PipelineModelWriter.super$save(Pipeline.scala:344)
at org.apache.spark.ml.PipelineModel$PipelineModelWriter.$anonfun$save$4(Pipeline.scala:344)
at org.apache.spark.ml.MLEvents.withSaveInstanceEvent(events.scala:174)
at org.apache.spark.ml.MLEvents.withSaveInstanceEvent$(events.scala:169)
at org.apache.spark.ml.util.Instrumentation.withSaveInstanceEvent(Instrumentation.scala:42)
at org.apache.spark.ml.PipelineModel$PipelineModelWriter.$anonfun$save$3(Pipeline.scala:344)
at org.apache.spark.ml.PipelineModel$PipelineModelWriter.$anonfun$save$3$adapted(Pipeline.scala:344)
at org.apache.spark.ml.util.Instrumentation$.$anonfun$instrumented$1(Instrumentation.scala:191)
at scala.util.Try$.apply(Try.scala:213)
at org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:191)
at org.apache.spark.ml.PipelineModel$PipelineModelWriter.save(Pipeline.scala:344)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.lang.Thread.run(Thread.java:748)
```

If the bug pertains to a specific feature please tag the appropriate [CODEOWNER](https://github.com/Microsoft/SynapseML/blob/master/CODEOWNERS) for better visibility

**Additional context**
Add any other context about the problem here.

Start by reproducing the failure with the provided PySpark pipeline and SynapseML 0.9.4 setup. Trace the save path through PipelineModel, Pipeline.SharedReadWrite, and ComplexParamsWriter using the shown NoSuchMethodError, then verify that the complete pipeline can be saved successfully to model_file.

Written by the indexing model from the issue text.