How to install to EMR from maven repository to /usr/lib/spark/jars

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### SynapseML version

1.0.10

### System information

- **Language version** (e.g. python 3.8, scala 2.12): python 3.9
- **Spark Version** (e.g. 3.2.3): 3.5.1
- **Spark Platform** (e.g. Synapse, Databricks): AWS EMR Release 7.3.1

### Describe the problem

Now I would like to try to install SynapseML to EMR for pyspark. If we execute configuration based on the below command on Jupyter notebooks that is work.
```
%%configure -f
{
"name": "synapseml",
"conf": {
"spark.jars.packages": "com.microsoft.azure:synapseml_2.12:1.0.9-spark3.5",
"spark.jars.repositories": "https://mmlspark.azureedge.net/maven"
}
}
```

But in production, we don't use Jupyter notebooks. Therefore, we first download corresponding jars from [maven repository](https://mvnrepository.com/search?q=synapseml) and copy to the path `/usr/lib/spark/jars` on EMR and do not work and show `com.microsoft.azure.synapse.ml.isolationforest.IsolationForest does not exist in the JVM`

Have anyone know what is the root cause result in this? Thank you.

### Code to reproduce issue

```python
from synapse.ml.isolationforest import IsolationForest

# print(type(IsolationForest))
hyper_params = {
'n_estimators': 100,
'max_samples': 32
'max_features': 1,
'bootstrap': False,
'contamination': 0.1,
}

isolation_forest_model = (
IsolationForest()
.setNumEstimators(hyper_params["n_estimators"])
.setBootstrap(hyper_params["bootstrap"])
.setMaxSamples(hyper_params["max_samples"])
.setMaxFeatures(hyper_params["max_features"])
.setFeaturesCol("features")
.setPredictionCol("predictedLabel")
.setScoreCol("outlierScore")
.setContamination(hyper_params["contamination"])
.setContaminationError(0.01 * hyper_params["contamination"])
)
```

### Other info / logs

```python-traceback
An error was encountered:
com.microsoft.azure.synapse.ml.isolationforest.IsolationForest does not exist in the JVM
Traceback (most recent call last):
File "/mnt1/yarn/usercache/livy/appcache/application_1742368398137_0002/container_1742368398137_0002_01_000001/pyspark.zip/pyspark/__init__.py", line 139, in wrapper
return func(self, **kwargs)
File "/mnt1/yarn/usercache/livy/appcache/application_1742368398137_0002/container_1742368398137_0002_01_000001/com.microsoft.azure_synapseml-core_2.12-1.0.9-spark3.5.jar/synapse/ml/isolationforest/IsolationForest.py", line 78, in __init__
self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.isolationforest.IsolationForest", self.uid)
File "/mnt1/yarn/usercache/livy/appcache/application_1742368398137_0002/container_1742368398137_0002_01_000001/pyspark.zip/pyspark/ml/wrapper.py", line 84, in _new_java_obj
java_obj = getattr(java_obj, name)
File "/mnt1/yarn/usercache/livy/appcache/application_1742368398137_0002/container_1742368398137_0002_01_000001/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1664, in __getattr__
raise Py4JError("{0} does not exist in the JVM".format(new_fqn))
py4j.protocol.Py4JError: com.microsoft.azure.synapse.ml.isolationforest.IsolationForest does not exist in the JVM
```

### What component(s) does this bug affect?

- [ ] `area/cognitive`: Cognitive project
- [ ] `area/core`: Core project
- [ ] `area/deep-learning`: DeepLearning project
- [ ] `area/lightgbm`: Lightgbm project
- [ ] `area/opencv`: Opencv project
- [ ] `area/vw`: VW project
- [ ] `area/website`: Website
- [ ] `area/build`: Project build system
- [ ] `area/notebooks`: Samples under notebooks folder
- [ ] `area/docker`: Docker usage
- [x] `area/models`: models related issue

### What language(s) does this bug affect?

- [ ] `language/scala`: Scala source code
- [x] `language/python`: Pyspark APIs
- [ ] `language/r`: R APIs
- [ ] `language/csharp`: .NET APIs
- [ ] `language/new`: Proposals for new client languages

### What integration(s) does this bug affect?

- [ ] `integrations/synapse`: Azure Synapse integrations
- [ ] `integrations/azureml`: Azure ML integrations
- [ ] `integrations/databricks`: Databricks integrations

Start with synapse/ml/isolationforest/IsolationForest.py and the JVM class name shown in the traceback, then compare the working spark.jars.packages configuration with the jars copied to /usr/lib/spark/jars on AWS EMR. Verify that the required SynapseML model classes are available to the JVM and that IsolationForest can be instantiated through the PySpark API.

Written by the indexing model from the issue text.