### Description

Add feature to EmrStepSensor to bring back the spark task url & logs after task execution

### Use case/motivation

After starting an EMR step task using EmrAddStepsOperator we generally have an EmrStepSensor to track the status of the step. The job ID is available for the sensor and is being poked at regular interval. 

```
[2022-04-26, 22:07:43 UTC] {base_aws.py:100} INFO - Retrieving region_name from Connection.extra_config['region_name']
[2022-04-26, 22:07:44 UTC] {emr.py:316} INFO - Poking step s-123ABC123ABC on cluster j-123ABC123ABC
[2022-04-26, 22:07:44 UTC] {emr.py:74} INFO - Job flow currently PENDING
[2022-04-26, 22:08:44 UTC] {emr.py:316} INFO - Poking step s-123ABC123ABC on cluster j-123ABC123ABC
[2022-04-26, 22:08:44 UTC] {emr.py:74} INFO - Job flow currently PENDING
[2022-04-26, 22:09:44 UTC] {emr.py:316} INFO - Poking step s-123ABC123ABC on cluster j-123ABC123ABC
[2022-04-26, 22:09:44 UTC] {emr.py:74} INFO - Job flow currently COMPLETED
[2022-04-26, 22:09:44 UTC] {base.py:251} INFO - Success criteria met. Exiting.
[2022-04-26, 22:09:44 UTC] {taskinstance.py:1288} INFO - Marking task as SUCCESS. dag_id=datapipeline_sample, task_id=calculate_pi_watch_step, execution_date=20220426T220739, start_date=20220426T220743, end_date=20220426T220944
```

After the task is completed the status is displayed. If the user wants to review the logs of the task, it is a multistep process to get hold of the job logs from EMR cluster. 

It will be a great addition to add the log url and possibly relay the logs to Airflow EmrStepSensor post completion of the task. This will be very handy when there are failures of many tasks and will make it a great user experience. 


### Related issues

_No response_

### Are you willing to submit a PR?

- [ ] 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)

key = "key-1"
        mocked_s3_res.Object(s3_bucket, key).put(Body=b"Data")
        hook = S3Hook()
        hook.delete_objects(bucket=s3_bucket, keys=[key])
        assert [o.key for o in mocked_s3_res.Bucket(s3_bucket).objects.all()] == []
    def test_delete_objects_many_keys(self, mocked_s3_res, s3_bucket):
        num_keys_to_remove = 1001
        keys = []
        for index in range(num_keys_to_remove):
            key = f"key-{index}"
            mocked_s3_res.Object(s3_bucket, key).put(Body=b"Data")
            keys.append(key)
        assert sum(1 for _ in mocked_s3_res.Bucket(s3_bucket).objects.all()) == num_keys_to_remove
        hook = S3Hook()
        hook.delete_objects(bucket=s3_bucket, keys=keys)
        assert [o.key for o in mocked_s3_res.Bucket(s3_bucket).objects.all()] == []
    def test_unify_bucket_name_and_key(self):
        class FakeS3Hook(S3Hook):
            @unify_bucket_name_and_key
            def test_function_with_wildcard_key(self, wildcard_key, bucket_name=None):
                return bucket_name, wildcard_key
            @unify_bucket_name_and_key
            def test_function_with_key(self, key, bucket_name=None):
                return bucket_name, key
            @unify_bucket_name_and_key
            def test_function_with_test_key(self, test_key, bucket_name=None):