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**Description**

When using decorators to define dags, e.g. dag_1.py:

```python
from airflow.decorators import dag, task
from airflow.utils.dates import days_ago

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


@task
def some_task():
    pass


@dag(
    default_args=DEFAULT_ARGS,
    schedule_interval=None,
    start_date=days_ago(2),
)
def my_dag():
    some_task()


DAG_1 = my_dag()

```

and

dag_2.py:

```python
from airflow.decorators import dag, task
from airflow.utils.dates import days_ago

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


@task
def some_other_task():
    pass


@dag(
    default_args=DEFAULT_ARGS,
    schedule_interval=None,
    start_date=days_ago(2),
)
def my_dag():
    some_other_task()


DAG_2 = my_dag()

```

We have two different dags which have been written in isolation, but by sheer bad luck both define `my_dag()`. This seems fine for each file in isolation, but on the airflow UI, we only end up seeing one entry for `my_dag`, where it has picked up `dag_1.py` and ignored `dag_2.py`.

**Use case / motivation**

We currently end up with only one DAG showing up on the UI, and no indication as to why the other one hasn't appeared.

Suggestion: popup similar to 'DAG import error' to highlight what needs changing in one of the DAG files in order for both to show up ("DAG import error: duplicate dag names found - please review {duplicate files} and ensure all dag definitions are unique"?)

**Are you willing to submit a PR?**

No time to spare on this at present

**Related Issues**

I haven't found any related issues with the search function.
airflow/models/dagbag.py
from airflow.exceptions import AirflowClusterPolicyViolation, AirflowDagCycleException, SerializedDagNotFound
from airflow.stats import Stats
from airflow.utils import timezone
from airflow.utils.dag_cycle_tester import test_cycle
from airflow.utils.file import correct_maybe_zipped, list_py_file_paths, might_contain_dag
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.retries import MAX_DB_RETRIES, run_with_db_retries
from airflow.utils.session import provide_session
from airflow.utils.timeout import timeout
if TYPE_CHECKING:
    import pathlib
class FileLoadStat(NamedTuple):
    """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