**Apache Airflow version**: 1.10.15 (also applies to 2.X)

**Environment**:

- **Cloud provider or hardware configuration**: Astronomer deployment

**What happened**:

I am currently upgrading an Airflow deployment from 1.10.15 to 2.1.0. While doing so, I switched over from `airflow.contrib.operators.sagemaker_training_operator.SageMakerTrainingOperator` to `airflow.providers.amazon.aws.operators.sagemaker_training.SageMakerTrainingOperator`, and found that some DAGs started failing at every run after that.

I dug into the issue a bit, and found that the problem comes from the operator listing existing training jobs ([here](https://github.com/apache/airflow/blob/db63de626f53c9e0242f0752bb996d0e32ebf6ea/airflow/providers/amazon/aws/operators/sagemaker_training.py#L95)). This method calls `boto3`'s `list_training_jobs` over and over again, enough times to get rate limited with a single operator if the number of existing training jobs is high enough - AWS does not allow to delete existing jobs, so these can easily be in the hundreds if not more. Since the operator does not allow to pass `max_results` to the hook's method (although the method can take it), the default page size is used (10) and the number of requests can explode. With a single operator, I was able to mitigate the issue by using the standard retry handler (instead of the legacy handler) - see [doc](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/retries.html). However, even the standard retry handler does not help in our case, where we have a dozen operators firing at the same time. All of them got rate limited again, and I was unable to make the job succeed.

One technical fix would be to use a dedicated pool with 1 slot, thereby effectively running all training jobs sequentially. However that won't do in the real world: SageMaker jobs are often long-running, and we cannot afford to go from 1-2 hours when executing them in parallel, to 10-20 hours sequentially.

I believe (and this is somewhat opinionated) that the `SageMakerTrainingOperator` should **not** be responsible for renaming jobs, for two reasons: (1) single responsibility principle (my operator should trigger a SageMaker job, not figure out the correct name + trigger it); (2) alignment between operators and the systems they interact with: running this list operation is, until AWS allows to somehow delete old jobs and/or dramatically increases rate limits, not aligned with the way AWS works.

**What you expected to happen**:

The `SageMakerTrainingOperator` should not be limited in parallelism by the number of existing training jobs in AWS. This limitation is a side-effect of listing existing training jobs. Therefore, the `SageMakerTrainingOperator` should not list existing training jobs.

**Anything else we need to know**:

<details><summary>x.log</summary>Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 984, in _run_raw_task
    result = task_copy.execute(context=context)
  File "/usr/local/lib/python3.7/site-packages/airflow/providers/amazon/aws/operators/sagemaker_training.py", line 97, in execute
    training_jobs = self.hook.list_training_jobs(name_contains=training_job_name)
  File "/usr/local/lib/python3.7/site-packages/airflow/providers/amazon/aws/hooks/sagemaker.py", line 888, in list_training_jobs
    list_training_jobs_request, "TrainingJobSummaries", max_results=max_results
  File "/usr/local/lib/python3.7/site-packages/airflow/providers/amazon/aws/hooks/sagemaker.py", line 945, in _list_request
    response = partial_func(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/botocore/client.py", line 337, in _api_call
    return self._make_api_call(operation_name, kwargs)
  File "/usr/local/lib/python3.7/site-packages/botocore/client.py", line 656, in _make_api_call
    raise error_class(parsed_response, operation_name)
botocore.exceptions.ClientError: An error occurred (ThrottlingException) when calling the ListTrainingJobs operation (reached max retries: 9): Rate exceeded</details>
tests/providers/amazon/aws/operators/test_sagemaker_training.py
mock_training.return_value = {
            'TrainingJobArn': 'testarn',
            'ResponseMetadata': {'HTTPStatusCode': 200},
        }
        self.sagemaker.execute(None)
        mock_training.assert_called_once_with(
            create_training_params,
            wait_for_completion=False,
            print_log=True,
            check_interval=5,
            max_ingestion_time=None,
        )
    @mock.patch.object(SageMakerHook, 'get_conn')
    @mock.patch.object(SageMakerHook, 'create_training_job')
    def test_execute_with_failure(self, mock_training, mock_client):
        mock_training.return_value = {
            'TrainingJobArn': 'testarn',
            'ResponseMetadata': {'HTTPStatusCode': 404},
        }
        with pytest.raises(AirflowException):
            self.sagemaker.execute(None)
    
    @mock.patch.object(SageMakerHook, "get_conn")
    @mock.patch.object(SageMakerHook, "list_training_jobs")
    @mock.patch.object(SageMakerHook, "create_training_job")
    def test_execute_with_existing_job_increment(