### 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)

max_ingestion_time: int | None = None,
    ):
        """
        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.
        :param config: the config for tuning
        :param wait_for_completion: if the program should keep running until job finishes
        :param check_interval: the time interval in seconds which the operator
            will check the status of any SageMaker job
        :param max_ingestion_time: the maximum ingestion time in seconds. Any
            SageMaker jobs that run longer than this will fail. Setting this to
            None implies no timeout for any SageMaker job.
        :return: A response to tuning job creation
        """
        self.check_tuning_config(config)
        response = self.get_conn().create_hyper_parameter_tuning_job(**config)
        if wait_for_completion:
            self.check_status(
                config["HyperParameterTuningJobName"],
                "HyperParameterTuningJobStatus",
                self.describe_tuning_job,
                check_interval,
                max_ingestion_time,
            )
        return response
    def create_transform_job(
        self,