### 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
"""Display DAG dependencies"""
        title = "DAG Dependencies"
        if not self.nodes or not self.edges:
            self._calculate_graph()
            self.last_refresh = timezone.utcnow()
        elif timezone.utcnow() > self.last_refresh + self.refresh_interval:
            max_last_updated = SerializedDagModel.get_max_last_updated_datetime()
            if max_last_updated is None or max_last_updated > self.last_refresh:
                self._calculate_graph()
            self.last_refresh = timezone.utcnow()
        return self.render_template(
            "airflow/dag_dependencies.html",
            title=title,
            nodes=self.nodes,
            edges=self.edges,
            last_refresh=self.last_refresh,
            arrange=conf.get("webserver", "dag_orientation"),
            width=request.args.get("width", "100%"),
            height=request.args.get("height", "800"),
        )
    def _calculate_graph(self):