### 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)

security_context = {
            "runAsUser": 1245,
        }
        k = KubernetesPodOperator(
            security_context=security_context,
            task_id="task",
        )
        pod = k.build_pod_request_obj(create_context(k))
        assert pod.spec.security_context == security_context
    def test_container_security_context(self):
        container_security_context = {"allowPrivilegeEscalation": False}
        k = KubernetesPodOperator(
            container_security_context=container_security_context,
            task_id="task",
        )
        pod = k.build_pod_request_obj(create_context(k))
        assert pod.spec.containers[0].security_context == container_security_context
    def test_envs_from_configmaps(self):
        env_from = [k8s.V1EnvFromSource(config_map_ref=k8s.V1ConfigMapEnvSource(name="test-config-map"))]
        k = KubernetesPodOperator(
            task_id="task",
            env_from=env_from,
        )
        pod = k.build_pod_request_obj(create_context(k))
        assert pod.spec.containers[0].env_from == env_from
    @pytest.mark.parametrize(("in_cluster",), ([True], [False]))
    @patch(HOOK_CLASS)
    def test_labels(self, hook_mock, in_cluster):