### Description

Add a process_line callback to KubernetesPodOperator 
Like https://github.com/apache/airflow/blob/main/airflow/providers/apache/beam/operators/beam.py#LL304C36-L304C57 the `BeamRunPythonPipelineOperator`, which allows the user to add stateful plugins based on the logging from docker job 

### Use case/motivation

I can add a plugin based on the logging and also allow cleanup on_kill based on the job creation log. 

### 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)

with mock.patch.object(PodLogsConsumer, "logs_available") as logs_available:
            logs_available.return_value = False
            consumer = PodLogsConsumer(
                response=mock.MagicMock(stream=mock.MagicMock(return_value=[b"message1", b"message2"])),
                pod=mock.MagicMock(),
                pod_manager=mock.MagicMock(container_is_running=mock.MagicMock(return_value=False)),
                container_name="base",
            )
            assert list(consumer) == []
    @pytest.mark.parametrize(
        "container_run, termination_time, now_time, post_termination_timeout, expected_logs_available",
        [
            (
                False,
                datetime(2022, 1, 1, 0, 0, 0, 0, tzinfo=utc),
                datetime(2022, 1, 1, 0, 1, 0, 0, tzinfo=utc),
                120,
                True,
            ),
            (
                False,
                datetime(2022, 1, 1, 0, 0, 0, 0, tzinfo=utc),
                datetime(2022, 1, 1, 0, 2, 0, 0, tzinfo=utc),
                120,
                False,
            ),
            (
                False,
                datetime(2022, 1, 1, 0, 0, 0, 0, tzinfo=utc),