### Body

There are some use cases where users want to trigger new DAG run as soon as one finished. This is a request I've seen several times with some variations (for example like this [Stackoverflow question](https://stackoverflow.com/q/75623153/14624409)) but the basic request is the same.

The workaround users do to get such functionality is place `TriggerDagRunOperator` as last task of their DAG invoking the same DAG:

```
from datetime import datetime

from airflow import DAG
from airflow.operators.empty import EmptyOperator
from airflow.operators.trigger_dagrun import TriggerDagRunOperator

with DAG(
    dag_id="example",
    start_date=datetime(2023, 1, 1,),
    catchup=False,
    schedule=None,
) as dag:
    task = EmptyOperator(task_id="first")
    trigger = TriggerDagRunOperator(
        task_id="trigger",
        trigger_dag_id="example",
    )

    task >> trigger

```

As you can see this works nicely:
![Screenshot 2023-03-03 at 14 20 51](https://user-images.githubusercontent.com/45845474/222718862-dfde8a41-24b6-4991-b318-b7f9784514f6.png)

My suggestion is to add first class support for this use case, so the above example will be changed to:

```

from datetime import datetime

from airflow import DAG
from airflow.operators.empty import EmptyOperator
from airflow.operators.trigger_dagrun import TriggerDagRunOperator

with DAG(
    dag_id="example",
    start_date=datetime(2023, 1, 1,),
    catchup=False,
    schedule="@continues",
) as dag:
    task = EmptyOperator(task_id="first")
```

I guess it won't exactly be `"@continues"` but more likely new [ScheduleArg](https://github.com/apache/airflow/blob/8b8552f5c4111fe0732067d7af06aa5285498a79/airflow/models/dag.py#L127) type but I show it like that just for simplification of the idea.

### Committer

- [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
if isinstance(child, AbstractOperator):
                    if child.task_id in dag.task_dict:
                        task = copied.children[child.task_id] = dag.task_dict[child.task_id]
                        task.task_group = proxy
                    else:
                        copied.used_group_ids.discard(child.task_id)
                else:
                    filtered_child = filter_task_group(child, proxy)
                    
                    if filtered_child.children:
                        copied.children[child.group_id] = filtered_child
            return copied
        dag._task_group = filter_task_group(self.task_group, None)
        
        
        subdag_task_groups = dag.task_group.get_task_group_dict()
        for group in subdag_task_groups.values():
            group.upstream_group_ids.intersection_update(subdag_task_groups)
            group.downstream_group_ids.intersection_update(subdag_task_groups)
            group.upstream_task_ids.intersection_update(dag.task_dict)
            group.downstream_task_ids.intersection_update(dag.task_dict)
        for t in dag.tasks:
            
            
            t.upstream_task_ids.intersection_update(dag.task_dict)
            t.downstream_task_ids.intersection_update(dag.task_dict)
        if len(dag.tasks) < len(self.tasks):
            dag.partial = True
        return dag
    def has_task(self, task_id: str):