**Description**

Currently we have **only** task level [cluster policy](https://airflow.readthedocs.io/en/latest/concepts.html?highlight=cluster%20policy#cluster-policy):
https://github.com/apache/airflow/blob/7dfb3980cecfafdb7d6b79d17455b08971cec7d4/airflow/models/dagbag.py#L386-L387

This is an amazing tool that allows users to validate their tasks by either skipping DAG or setting default values (queues, owners, emails etc). I would like to proposes same mechanism for DAGs.

Some may argue that users already can implement it by accessing `task.dag` attribute in the policy, however this has the following drawbacks:
- it's not "explicit over implicit" see https://github.com/apache/airflow/issues/12086#issuecomment-722986766
- the "DAG policy" is executed for every task (if there's 1000 tasks in DAG, the DAG policy will be checked 1000 times)

 **Use case / motivation**

Create explicit mechanism for creating "DAG level" cluster policy that will be optimised for this task (check only once per dag not for every task) and will not required users to implement this custom optimisation logic (for example by monkey patching or changes in their fork).

**Related Issues**

#10282

airflow/models/dagbag.py
"""Information about single file"""
    file: str
    duration: timedelta
    dag_num: int
    task_num: int
    dags: str
class DagBag(LoggingMixin):
    """
    A dagbag is a collection of dags, parsed out of a folder tree and has high
    level configuration settings, like what database to use as a backend and
    what executor to use to fire off tasks. This makes it easier to run
    distinct environments for say production and development, tests, or for
    different teams or security profiles. What would have been system level
    settings are now dagbag level so that one system can run multiple,
    independent settings sets.
    :param dag_folder: the folder to scan to find DAGs
    :type dag_folder: unicode
    :param include_examples: whether to include the examples that ship
        with airflow or not
    :type include_examples: bool
    :param include_smart_sensor: whether to include the smart sensor native
        DAGs that create the smart sensor operators for whole cluster
    :type include_smart_sensor: bool
    :param read_dags_from_db: Read DAGs from DB if ``True`` is passed.
        If ``False`` DAGs are read from python files.
    :type read_dags_from_db: bool
    """
    DAGBAG_IMPORT_TIMEOUT = conf.getfloat('core', 'DAGBAG_IMPORT_TIMEOUT')
    SCHEDULER_ZOMBIE_TASK_THRESHOLD = conf.getint('scheduler', 'scheduler_zombie_task_threshold')
    def __init__(