### Apache Airflow Provider(s)

amazon

### Versions of Apache Airflow Providers



apache-airflow-providers-amazon | 2.4.0

### Apache Airflow version

2.2.3 (latest released)

### Operating System

Amazon Linux 2

### Deployment

MWAA

### Deployment details

_No response_

### What happened

Sagemaker Processing Operator no longer honors the `action_if_job_exists` param and always fails creation of a new processing job is a job with the name already exists. 

This happens because in a recent change, the function responsible for executing the job no longer honors the increment setting:

Change that breaks the increment: https://github.com/apache/airflow/commit/96dd70348ad7e31cfeae6d21af70671b41551fe9

New code: https://github.com/apache/airflow/blob/6734eb1d09a99dc519e89a59e2086cef09a87098/airflow/providers/amazon/aws/operators/sagemaker.py#L167

  

### What you expected to happen

When Sagemaker Processing operator is called with a job-name that already exists, the job creation should succeed with a name that is incremented by 1.   

### How to reproduce

invoke SageMakerProcessingOperator twice with the same job name while keeping action_if_job_exists as 'increment'.   

### Anything else

_No response_

### Are you willing to submit PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

"RoleArn": "string",
        "InputDataConfig": create_training_params["InputDataConfig"],
        "OutputDataConfig": create_training_params["OutputDataConfig"],
        "ResourceConfig": create_training_params["ResourceConfig"],
        "StoppingCondition": dict(MaxRuntimeInSeconds=60 * 60),
    },
}
create_transform_params = {
    "TransformJobName": job_name,
    "ModelName": model_name,
    "BatchStrategy": "MultiRecord",
    "TransformInput": {"DataSource": {"S3DataSource": {"S3DataType": "S3Prefix", "S3Uri": data_url}}},
    "TransformOutput": {
        "S3OutputPath": output_url,
    },
    "TransformResources": {"InstanceType": "ml.m4.xlarge", "InstanceCount": 123},
}
create_transform_params_fs = {
    "TransformJobName": job_name,
    "ModelName": model_name,
    "BatchStrategy": "MultiRecord",
    "TransformInput": {
        "DataSource": {
            "FileSystemDataSource": {
                "DirectoryPath": "/tmp",
                "FileSystemAccessMode": "ro",
                "FileSystemId": "fs-abc",
                "FileSystemType": "FSxLustre",
            }
        }