### 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)

@pytest.mark.parametrize("active_deadline_seconds", [10, 20])
    def test_kubernetes_pod_operator_active_deadline_seconds(self, active_deadline_seconds):
        k = KubernetesPodOperator(
            task_id=f"test_task_{active_deadline_seconds}",
            active_deadline_seconds=active_deadline_seconds,
            image="busybox",
            cmds=["sh", "-c", "echo 'hello world' && sleep 60"],
            namespace="default",
            on_finish_action="keep_pod",
        )
        context = create_context(k)
        with pytest.raises(AirflowException):
            k.execute(context)
        pod = k.find_pod("default", context, exclude_checked=False)
        k8s_client = client.CoreV1Api()
        pod_status = k8s_client.read_namespaced_pod_status(name=pod.metadata.name, namespace="default")
        phase = pod_status.status.phase
        reason = pod_status.status.reason
        assert phase == "Failed"
        assert reason == "DeadlineExceeded"