java.lang.NoSuchMethodError: spray.json.BasicFormats.$init$(Lspray/json/BasicFormats;)V

**Describe the bug**
I am using Databricks DBR 8.3ML and not able to instantiate LightGBMClassifier. Followed the steps in the documentation

**To Reproduce**
Databricks DBR 8.3ML | Spark 3.1.1 | Scala 2.12
SynapseML installed Maven: com.microsoft.azure:synapseml_2.12:0.9.4

**Expected behavior**
Library installed successfully.
Below command executed sucessfully -
from synapse.ml.lightgbm import LightGBMClassifier

**Info (please complete the following information):**
- SynapseML Version: 0.9.4
- Spark Version - 3.1.1
- Spark Platform - Databricks DBR 8.3ML

** Stacktrace**
```
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
in
----> 1 m1 = LightGBMClassifier(objective="binary")

/databricks/spark/python/pyspark/__init__.py in wrapper(self, *args, **kwargs)
112 raise TypeError("Method %s forces keyword arguments." % func.__name__)
113 self._input_kwargs = kwargs
--> 114 return func(self, **kwargs)
115 return wrapper
116

/local_disk0/spark-e1502211-937e-4011-a1c8-38eb6e14ccc1/userFiles-c4de24ed-7b19-4da7-a212-a1877977fb1e/addedFile4981733457150739342synapseml_lightgbm_2_12_0_9_4-a075c.jar/synapse/ml/lightgbm/LightGBMClassifier.py in __init__(self, java_obj, baggingFraction, baggingFreq, baggingSeed, binSampleCount, boostFromAverage, boostingType, categoricalSlotIndexes, categoricalSlotNames, chunkSize, defaultListenPort, driverListenPort, dropRate, earlyStoppingRound, featureFraction, featuresCol, featuresShapCol, fobj, improvementTolerance, initScoreCol, isProvideTrainingMetric, isUnbalance, labelCol, lambdaL1, lambdaL2, leafPredictionCol, learningRate, matrixType, maxBin, maxBinByFeature, maxDeltaStep, maxDepth, maxDrop, metric, minDataInLeaf, minGainToSplit, minSumHessianInLeaf, modelString, negBaggingFraction, numBatches, numIterations, numLeaves, numTasks, numThreads, objective, parallelism, posBaggingFraction, predictionCol, probabilityCol, rawPredictionCol, repartitionByGroupingColumn, skipDrop, slotNames, thresholds, timeout, topK, uniformDrop, useBarrierExecutionMode, useSingleDatasetMode, validationIndicatorCol, verbosity, weightCol, xgboostDartMode)
283 super(LightGBMClassifier, self).__init__()
284 if java_obj is None:
--> 285 self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier", self.uid)
286 else:
287 self._java_obj = java_obj

/databricks/spark/python/pyspark/ml/wrapper.py in _new_java_obj(java_class, *args)
64 java_obj = getattr(java_obj, name)
65 java_args = [_py2java(sc, arg) for arg in args]
---> 66 return java_obj(*java_args)
67
68 @staticmethod

/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py in __call__(self, *args)
1566
1567 answer = self._gateway_client.send_command(command)
-> 1568 return_value = get_return_value(
1569 answer, self._gateway_client, None, self._fqn)
1570

/databricks/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
115 def deco(*a, **kw):
116 try:
--> 117 return f(*a, **kw)
118 except py4j.protocol.Py4JJavaError as e:
119 converted = convert_exception(e.java_exception)

/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
324 value = OUTPUT_CONVERTER[type](answer[2:], gateway_client)
325 if answer[1] == REFERENCE_TYPE:
--> 326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
328 format(target_id, ".", name), value)

Py4JJavaError: An error occurred while calling None.com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier.
: java.lang.NoSuchMethodError: spray.json.BasicFormats.$init$(Lspray/json/BasicFormats;)V
at com.microsoft.azure.synapse.ml.logging.LogJsonProtocol$.(BasicLogging.scala:18)
at com.microsoft.azure.synapse.ml.logging.LogJsonProtocol$.(BasicLogging.scala)
at com.microsoft.azure.synapse.ml.logging.BasicLogging.logBase(BasicLogging.scala:31)
at com.microsoft.azure.synapse.ml.logging.BasicLogging.logBase$(BasicLogging.scala:30)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier.logBase(LightGBMClassifier.scala:26)
at com.microsoft.azure.synapse.ml.logging.BasicLogging.logClass(BasicLogging.scala:41)
at com.microsoft.azure.synapse.ml.logging.BasicLogging.logClass$(BasicLogging.scala:40)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier.logClass(LightGBMClassifier.scala:26)
at com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier.(LightGBMClassifier.scala:29)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)
at py4j.Gateway.invoke(Gateway.java:250)
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
at py4j.GatewayConnection.run(GatewayConnection.java:251)
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.

AB#1984494

Start by reproducing the LightGBMClassifier construction in the reported Databricks DBR 8.3ML, Spark 3.1.1, Scala 2.12, and SynapseML 0.9.4 environment. Inspect the stacktrace references to BasicLogging.scala:18 and LightGBMClassifier.scala:29, then check the dependency versions involved in spray.json. Done means the classifier instantiates without the NoSuchMethodError.

Written by the indexing model from the issue text.