### 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 training job info associated with the name
        :param name: the name of the training job
        :return: A dict contains all the training job info
        """
        return self.get_conn().describe_training_job(TrainingJobName=name)
    def describe_training_job_with_log(
        self,
        job_name: str,
        positions,
        stream_names: list,
        instance_count: int,
        state: int,
        last_description: dict,
        last_describe_job_call: float,
    ):
        """Return the training job info associated with job_name and print CloudWatch logs"""
        log_group = "/aws/sagemaker/TrainingJobs"
        if len(stream_names) < instance_count:
            
            
            logs_conn = self.logs_hook.get_conn()
            try:
                streams = logs_conn.describe_log_streams(
                    logGroupName=log_group,
                    logStreamNamePrefix=job_name + "/",
                    orderBy="LogStreamName",
                    limit=instance_count,
                )
                stream_names = [s["logStreamName"] for s in streams["logStreams"]]