**Apache Airflow version**: 2.0.1
**Kubernetes version**: v1.19.7
**Executor**: KubernetesExecutor

**What happened**:

If you have a worker that gets stuck in pending forever, say with a missing volume mount, the task will stay in the queued state forever. Nothing is applying a timeout on it actually being able to start.

**What you expected to happen**:

Eventually the scheduler will notice that the worker hasn't progressed past pending after a given amount of time and will mark it as a failure.

**How to reproduce it**:
```python
from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.utils.dates import days_ago
from kubernetes.client import models as k8s


default_args = {
    "owner": "airflow",
}

with DAG(
    dag_id="pending",
    default_args=default_args,
    schedule_interval=None,
    start_date=days_ago(2),
) as dag:
    BashOperator(
        task_id="forever_pending",
        bash_command="date; sleep 30; date",
        executor_config={
            "pod_override": k8s.V1Pod(
                spec=k8s.V1PodSpec(
                    containers=[
                        k8s.V1Container(
                            name="base",
                            volume_mounts=[
                                k8s.V1VolumeMount(mount_path="/foo/", name="vol")
                            ],
                        )
                    ],
                    volumes=[
                        k8s.V1Volume(
                            name="vol",
                            persistent_volume_claim=k8s.V1PersistentVolumeClaimVolumeSource(
                                claim_name="missing"
                            ),
                        )
                    ],
                )
            ),
        },
    )
```


**Anything else we need to know**:
Related to:

* #15149 (This is reporting that these pending pods don't get deleted via "Mark Failed")
* #14556 (This handles when these pending pods get deleted and is already fixed)

tests/executors/test_kubernetes_executor.py
cases = [
            ("my_dag_id", "my-task-id"),
            ("my.dag.id", "my.task.id"),
            ("MYDAGID", "MYTASKID"),
            ("my_dag_id", "my_task_id"),
            ("mydagid" * 200, "my_task_id" * 200),
        ]
        cases.extend(
            [(self._gen_random_string(seed, 200), self._gen_random_string(seed, 200)) for seed in range(100)]
        )
        return cases
    @staticmethod
    def _is_valid_pod_id(name):
        regex = r"^[a-z0-9]([-a-z0-9]*[a-z0-9])?(\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*$"
        return len(name) <= 253 and all(ch.lower() == ch for ch in name) and re.match(regex, name)
    @staticmethod
    def _is_safe_label_value(value):
        regex = r'^[^a-z0-9A-Z]*|[^a-zA-Z0-9_\-\.]|[^a-z0-9A-Z]*$'
        return len(value) <= 63 and re.match(regex, value)
    @unittest.skipIf(AirflowKubernetesScheduler is None, 'kubernetes python package is not installed')
    def test_create_pod_id(self):
        for dag_id, task_id in self._cases():
            pod_name = PodGenerator.make_unique_pod_id(create_pod_id(dag_id, task_id))
            assert self._is_valid_pod_id(pod_name)
    @unittest.skipIf(AirflowKubernetesScheduler is None, 'kubernetes python package is not installed')
    @mock.patch("airflow.kubernetes.pod_generator.PodGenerator")
    @mock.patch("airflow.executors.kubernetes_executor.KubeConfig")
    def test_get_base_pod_from_template(self, mock_kubeconfig, mock_generator):