### 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)

"TrainingStartTime": datetime(2018, 2, 17, 7, 15, 0, 103000),
    "TrainingEndTime": datetime(2018, 2, 17, 7, 19, 34, 953000),
    "ResponseMetadata": {
        "HTTPStatusCode": 200,
    },
}
DESCRIBE_TRAINING_INPROGRESS_RETURN = dict(DESCRIBE_TRAINING_COMPLETED_RETURN)
DESCRIBE_TRAINING_INPROGRESS_RETURN.update({"TrainingJobStatus": "InProgress"})
DESCRIBE_TRAINING_FAILED_RETURN = dict(DESCRIBE_TRAINING_COMPLETED_RETURN)
DESCRIBE_TRAINING_FAILED_RETURN.update({"TrainingJobStatus": "Failed", "FailureReason": "Unknown"})
DESCRIBE_TRAINING_STOPPING_RETURN = dict(DESCRIBE_TRAINING_COMPLETED_RETURN)
DESCRIBE_TRAINING_STOPPING_RETURN.update({"TrainingJobStatus": "Stopping"})
message = "message"
status = "status"
SECONDARY_STATUS_DESCRIPTION_1 = {
    "SecondaryStatusTransitions": [{"StatusMessage": message, "Status": status}]
}
SECONDARY_STATUS_DESCRIPTION_2 = {
    "SecondaryStatusTransitions": [{"StatusMessage": "different message", "Status": status}]
}
DEFAULT_LOG_STREAMS = {"logStreams": [{"logStreamName": job_name + "/xxxxxxxxx"}]}
LIFECYCLE_LOG_STREAMS = [
    DEFAULT_LOG_STREAMS,
    DEFAULT_LOG_STREAMS,
    DEFAULT_LOG_STREAMS,
    DEFAULT_LOG_STREAMS,
    DEFAULT_LOG_STREAMS,
    DEFAULT_LOG_STREAMS,
]
DEFAULT_LOG_EVENTS = [