KMS key not supported for FrameworkProcessor.run()

[huyqd]

Describe the bug
Cannot start a FrameworkProcessor (e.g. XGBoostProcessor) Processing Job using a custom-kms-encrypted S3 bucket.
I think the problem stems from this method here, it doesn't inherit the provided kms key

To reproduce

• Create a KMS key
• Add S3 bucket policy to only allow s3:PutObject action with the above KMS key

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "RestrictToDefaultOrKMS",
            "Effect": "Deny",
            "Principal": "*",
            "Action": "s3:PutObject",
            "Resource": "<bucket-arn>/*",
            "Condition": {
                "Null": {
                    "s3:x-amz-server-side-encryption": "false"
                },
                "StringNotEquals": {
                    "s3:x-amz-server-side-encryption": "aws:kms"
                }
            }
        },
        {
            "Sid": "Restrict_KMS_Key",
            "Effect": "Deny",
            "Principal": "*",
            "Action": "s3:PutObject",
            "Resource": "<bucket-arn>/*",
            "Condition": {
                "StringNotEqualsIfExists": {
                    "s3:x-amz-server-side-encryption-aws-kms-key-id": <kms-key-arn>
                }
            }
        }
    ]
}

• Start a Sagemaker Processing Job with FrameworkProcessor, based on the processing step here

region = sagemaker.Session().boto_region_name
sm_client = boto3.client("sagemaker")
boto_session = boto3.Session(region_name=region)
sagemaker_session = sagemaker.session.Session(
    boto_session=boto_session,
    sagemaker_client=sm_client,
    default_bucket=<bucket-name>,
)
processor = XGBoostProcessor(
    framework_version="1.3-1",
    py_version="py3",
    instance_type="ml.m5.xlarge",
    instance_count=1,
    sagemaker_session=sagemaker_session,
    role=role,
    output_kms_key=<kms-key-arn>,
)

processor_run_args = sklearn_processor.run(
    kms_key=kms_key,
    outputs=[
        ProcessingOutput(
            output_name="train",
            source="/opt/ml/processing/train",
            destination=f"s3://{default_bucket}/PreprocessAbaloneDataForHPO"
        ),
        ProcessingOutput(
            output_name="validation",
            source="/opt/ml/processing/validation",
            destination=f"s3://{default_bucket}/PreprocessAbaloneDataForHPO"
        ),
        ProcessingOutput(
            output_name="test",
            source="/opt/ml/processing/test",
            destination=f"s3://{default_bucket}/PreprocessAbaloneDataForHPO"
        ),
    ],
    code="code/preprocess.py",
    arguments=["--input-data", "s3://sagemaker-sample-files/datasets/tabular/uci_abalone/abalone.csv"],
)

Expected behavior
Job starts and runs normally

Screenshots or logs
The error that I get

System information
A description of your system. Please provide:

• SageMaker Python SDK version: 2.135.1
• Framework name (eg. PyTorch) or algorithm (eg. KMeans): XGBoostProcessor (or any FrameworkProcessor)
• Framework version: 1.3-1
• Python version: py3
• CPU or GPU: cpu
• Custom Docker image (Y/N): No

Additional context
Add any other context about the problem here.