**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
self, old_states: List[str], new_state: str, session: Session = None
    ) -> None:
        """
        For all DAG IDs in the DagBag, look for task instances in the
        old_states and set them to new_state if the corresponding DagRun
        does not exist or exists but is not in the running state. This
        normally should not happen, but it can if the state of DagRuns are
        changed manually.
        :param old_states: examine TaskInstances in this state
        :type old_states: list[airflow.utils.state.State]
        :param new_state: set TaskInstances to this state
        :type new_state: airflow.utils.state.State
        """
        tis_changed = 0
        query = (
            session.query(models.TaskInstance)
            .outerjoin(models.TaskInstance.dag_run)
            .filter(models.TaskInstance.dag_id.in_(list(self.dagbag.dag_ids)))
            .filter(models.TaskInstance.state.in_(old_states))
            .filter(