### 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)

"""
        Return the tuning job info associated with the name
        :param name: the name of the tuning job
        :return: A dict contains all the tuning job info
        """
        return self.get_conn().describe_hyper_parameter_tuning_job(HyperParameterTuningJobName=name)
    def describe_model(self, name: str) -> dict:
        """
        Return the SageMaker model info associated with the name
        :param name: the name of the SageMaker model
        :return: A dict contains all the model info
        """
        return self.get_conn().describe_model(ModelName=name)
    def describe_transform_job(self, name: str) -> dict:
        """
        Return the transform job info associated with the name
        :param name: the name of the transform job
        :return: A dict contains all the transform job info
        """
        return self.get_conn().describe_transform_job(TransformJobName=name)
    def describe_processing_job(self, name: str) -> dict:
        """
        Return the processing job info associated with the name
        :param name: the name of the processing job
        :return: A dict contains all the processing job info
        """
        return self.get_conn().describe_processing_job(ProcessingJobName=name)
    def describe_endpoint_config(self, name: str) -> dict: