**Apache Airflow version**: 2.0.2


**Kubernetes version (if you are using kubernetes)** (use `kubectl version`): NA

**Environment**: MWAA and Locally

- **Cloud provider or hardware configuration**: AWS
- **OS** (e.g. from /etc/os-release): NA
- **Kernel** (e.g. `uname -a`): NA
- **Install tools**: NA
- **Others**: NA

**What happened**:

When calling the SagemakerProcessingOperator sometimes get: "botocore.exceptions.ClientError: An error occurred (ThrottlingException)" due to excessive ListProcessingJobs operations.

**What you expected to happen**:

The job should have started without timing out. I believe one fix would be to use the `NameContains` functionality of boto3 [list_processing_jobs](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.list_processing_jobs) so you don't have to paginate as is occurring [here](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/hooks/sagemaker.py#L916). 

**How to reproduce it**:

If you incrementally create Sagemaker Processing jobs you will eventually see the Throttling as the pagination increases.

**Anything else we need to know**:

This looks like it is happening when the account already has a lot of former Sagemaker Processing jobs.
airflow/providers/amazon/aws/hooks/sagemaker.py
:return: response of describe call after job is done
        """
        if not non_terminal_states:
            non_terminal_states = self.non_terminal_states
        sec = 0
        running = True
        while running:
            time.sleep(check_interval)
            sec += check_interval
            try:
                response = describe_function(job_name)
                status = response[key]
                self.log.info('Job still running for %s seconds... current status is %s', sec, status)
            except KeyError:
                raise AirflowException('Could not get status of the SageMaker job')
            except ClientError:
                raise AirflowException('AWS request failed, check logs for more info')
            if status in non_terminal_states:
                running = True
            elif status in self.failed_states:
                raise AirflowException(f"SageMaker job failed because {response['FailureReason']}")
            else:
                running = False
            if max_ingestion_time and sec > max_ingestion_time:
                
                raise AirflowException(f'SageMaker job took more than {max_ingestion_time} seconds')
        self.log.info('SageMaker Job completed')
        response = describe_function(job_name)
        return response
    def check_training_status_with_log(