### Description

Make resources in KubernetesPodOperator as templated. We need to modify this during several runs and it needs code change for each run.

### Use case/motivation

For running CPU and memory intensive workloads, we want to continuously optimise the "limt_cpu" and "limit_memory" parameters. Hence, we want to provide these parameters as a part of the pipeline definition.

### Related issues

_No response_

### Are you willing to submit a PR?

- [X] 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)

k = KubernetesPodOperator(
            name="test",
            task_id="task",
        )
        pod = k.build_pod_request_obj(create_context(k, map_index=10))
        assert pod.metadata.labels == {
            "dag_id": "dag",
            "kubernetes_pod_operator": "True",
            "task_id": "task",
            "try_number": "1",
            "airflow_version": mock.ANY,
            "run_id": "test",
            "map_index": "10",
            "airflow_kpo_in_cluster": str(k.hook.is_in_cluster),
        }
    @patch(HOOK_CLASS, new=MagicMock)
    def test_find_pod_labels(self):