### 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(
            task_id=".hi.-_09HI",
            random_name_suffix=False,
        )
        pod = k.build_pod_request_obj({})
        assert pod.metadata.name == "0.hi.--09hi"
    def test_task_id_as_name_with_suffix(self):
        k = KubernetesPodOperator(
            task_id=".hi.-_09HI",
            random_name_suffix=True,
        )
        pod = k.build_pod_request_obj({})
        expected = "0.hi.--09hi"
        assert pod.metadata.name.startswith(expected)
        assert re.match(rf"{expected}-[a-z0-9-]+", pod.metadata.name) is not None
    def test_task_id_as_name_with_suffix_very_long(self):
        k = KubernetesPodOperator(
            task_id="a" * 250,
            random_name_suffix=True,
        )
        pod = k.build_pod_request_obj({})
        assert re.match(r"aaaaaaaaaaaaaaaaaaaaaaaaaaaaaa-[a-z0-9-]+", pod.metadata.name) is not None
    def test_task_id_as_name_dag_id_is_ignored(self):