### 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)

"""
    Starts a hyperparameter tuning job. A hyperparameter tuning job finds the
    best version of a model by running many training jobs on your dataset using
    the algorithm you choose and values for hyperparameters within ranges that
    you specify. It then chooses the hyperparameter values that result in a model
    that performs the best, as measured by an objective metric that you choose.
    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:SageMakerTuningOperator`
    :param config: The configuration necessary to start a tuning job (templated).
        For details of the configuration parameter see
        :py:meth:`SageMaker.Client.create_hyper_parameter_tuning_job`
    :param aws_conn_id: The AWS connection ID to use.
    :param wait_for_completion: Set to True to wait until the tuning job finishes.
    :param check_interval: If wait is set to True, the time interval, in seconds,
        that this operation waits to check the status of the tuning job.
    :param max_ingestion_time: If wait is set to True, the operation fails
        if the tuning job doesn't finish within max_ingestion_time seconds. If you
        set this parameter to None, the operation does not timeout.
    :return Dict: Returns The ARN of the tuning job created in Amazon SageMaker.
    """
    def __init__(