**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
current_transitions = job_description.get('SecondaryStatusTransitions')
    if current_transitions is None or len(current_transitions) == 0:
        return ''
    prev_transitions_num = 0
    if prev_description is not None:
        if prev_description.get('SecondaryStatusTransitions') is not None:
            prev_transitions_num = len(prev_description['SecondaryStatusTransitions'])
    transitions_to_print = (
        current_transitions[-1:]
        if len(current_transitions) == prev_transitions_num
        else current_transitions[prev_transitions_num - len(current_transitions) :]
    )
    status_strs = []
    for transition in transitions_to_print:
        message = transition['StatusMessage']
        time_str = timezone.convert_to_utc(job_description['LastModifiedTime']).strftime('%Y-%m-%d %H:%M:%S')
        status_strs.append(f"{time_str} {transition['Status']} - {message}")
    return '\n'.join(status_strs)
class SageMakerHook(AwsBaseHook):
    """
    Interact with Amazon SageMaker.
    Additional arguments (such as ``aws_conn_id``) may be specified and
    are passed down to the underlying AwsBaseHook.
    .. seealso::
        :class:`~airflow.providers.amazon.aws.hooks.base_aws.AwsBaseHook`
    """
    non_terminal_states = {'InProgress', 'Stopping'}
    endpoint_non_terminal_states = {'Creating', 'Updating', 'SystemUpdating', 'RollingBack', 'Deleting'}
    failed_states = {'Failed'}
    def __init__(self, *args, **kwargs):