**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)

airflow/jobs/scheduler_job.py
try:
            sig_name = signal.Signals(signum).name  
        except Exception:  
            sig_name = str(signum)
        self.log.info("%s\n%s received, printing debug\n%s", "-" * 80, sig_name, "-" * 80)
        self.executor.debug_dump()
        self.log.info("-" * 80)
    def is_alive(self, grace_multiplier: Optional[float] = None) -> bool:
        """
        Is this SchedulerJob alive?
        We define alive as in a state of running and a heartbeat within the
        threshold defined in the ``scheduler_health_check_threshold`` config
        setting.
        ``grace_multiplier`` is accepted for compatibility with the parent class.
        :rtype: boolean
        """
        if grace_multiplier is not None:
            
            return super().is_alive(grace_multiplier=grace_multiplier)
        scheduler_health_check_threshold: int = conf.getint('scheduler', 'scheduler_health_check_threshold')
        return (
            self.state == State.RUNNING
            and (timezone.utcnow() - self.latest_heartbeat).total_seconds() < scheduler_health_check_threshold
        )
    @provide_session
    def _change_state_for_tis_without_dagrun(