**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
"""
    Enum-style class holding all possible states of CloudWatch log streams.
    https://sagemaker.readthedocs.io/en/stable/session.html#sagemaker.session.LogState
    """
    STARTING = 1
    WAIT_IN_PROGRESS = 2
    TAILING = 3
    JOB_COMPLETE = 4
    COMPLETE = 5
Position = collections.namedtuple('Position', ['timestamp', 'skip'])
def argmin(arr, f: Callable) -> Optional[int]:
    """Return the index, i, in arr that minimizes f(arr[i])"""
    min_value = None
    min_idx = None
    for idx, item in enumerate(arr):
        if item is not None:
            if min_value is None or f(item) < min_value:
                min_value = f(item)
                min_idx = idx
    return min_idx
def secondary_training_status_changed(current_job_description: dict, prev_job_description: dict) -> bool: