### Description

https://github.com/apache/airflow/pull/17946 implements a `/robots.txt` endpoint to block search engines crawling Airflow - in the cases where it is (accidentally) exposed to the public Internet.

If we record any GET requests to that end-point we'd have a strong warning flag that the deployment is exposed, and could issue a warning in the UI, or even enable some kill-switch on the deployment. 

Some deployments are likely intentionally available and rely on auth mechanisms on the `login` endpoint, so there should be a config option to suppress the warnings.

An alternative approach would be to monitor for requests from specific user-agents used by crawlers for the same reasons

### Use case/motivation

People who accidentally expose airflow have a slightly higher chance of realising they've done so and tighten their security.

### Related issues

_No response_

### Are you willing to submit a PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

airflow/www/views.py
nodes: List[Dict[str, Any]] = []
        edges: List[Dict[str, str]] = []
        for dag, dependencies in SerializedDagModel.get_dag_dependencies().items():
            dag_node_id = f"dag:{dag}"
            nodes.append(self._node_dict(dag_node_id, dag, "dag"))
            for dep in dependencies:
                nodes.append(self._node_dict(dep.node_id, dep.dependency_id, dep.dependency_type))
                edges.extend(
                    [
                        {"u": f"dag:{dep.source}", "v": dep.node_id},
                        {"u": dep.node_id, "v": f"dag:{dep.target}"},
                    ]
                )
        self.nodes = nodes
        self.edges = edges
    @staticmethod
    def _node_dict(node_id, label, node_class):
        return {
            "id": node_id,
            "value": {"label": label, "rx": 5, "ry": 5, "class": node_class},
        }
class ActionModelView(PermissionModelView):