### 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)

"""
    Use Amazon SageMaker Processing to analyze data and evaluate machine learning
    models on Amazon SageMake. With Processing, you can use a simplified, managed
    experience on SageMaker to run your data processing workloads, such as feature
    engineering, data validation, model evaluation, and model interpretation.
     .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:SageMakerProcessingOperator`
    :param config: The configuration necessary to start a processing job (templated).
        For details of the configuration parameter see :py:meth:`SageMaker.Client.create_processing_job`
    :param aws_conn_id: The AWS connection ID to use.
    :param wait_for_completion: If wait is set to True, the time interval, in seconds,
        that the operation waits to check the status of the processing job.
    :param print_log: if the operator should print the cloudwatch log during processing
    :param check_interval: if wait is set to be true, this is the time interval
        in seconds which the operator will check the status of the processing job
    :param max_ingestion_time: If wait is set to True, the operation fails if the processing job
        doesn't finish within max_ingestion_time seconds. If you set this parameter to None,
        the operation does not timeout.
    :param action_if_job_exists: Behaviour if the job name already exists. Possible options are "increment"
        (default) and "fail".
    :return Dict: Returns The ARN of the processing job created in Amazon SageMaker.
    """
    def __init__(