### Description

This applies to the following APIs:
- [x] open anomaly detection
- [x] start datafeed
- [x] start data frame analytics job
- [x] start trained model deployment

Currently, if the operation takes longer than the configured `timeout`, the response status code is `500`.

With Kibana in mind, a `4xx` response would be more suitable as it will lead to showing more useful information to the user about why the request failed.

Looking at [RFC 9110](https://www.rfc-editor.org/rfc/rfc9110.html#name-408-request-timeout), it seems the most appropriate `4xx` code would be `408 Request Timeout`, even though the RFC describes a different situation. 

The alternative, `504 Gateway Timeout`, would be more appropriate for this situation but it wouldn't improve the user experience in Kibana.
x-pack/plugin/ml/qa/native-multi-node-tests/src/javaRestTest/java/org/elasticsearch/xpack/ml/integration/MlJobIT.java
EntityUtils.toString(client().performRequest(new Request("GET", indexName + "/_count")).getEntity()),
            containsString("\"count\":0")
        );
        expectThrows(
            ResponseException.class,
            () -> client().performRequest(new Request("GET", MachineLearning.BASE_PATH + "anomaly_detectors/" + jobId + "/_stats"))
        );
    }
    public void testDelete_multipleRequest() throws Exception {
        String jobId = "delete-job-multiple-times";
        createFarequoteJob(jobId);
        Map<Long, Response> responses = ConcurrentCollections.newConcurrentMap();
        Map<Long, ResponseException> responseExceptions = ConcurrentCollections.newConcurrentMap();
        AtomicReference<IOException> ioe = new AtomicReference<>();
        AtomicInteger recreationGuard = new AtomicInteger(0);
        AtomicReference<Response> recreationResponse = new AtomicReference<>();
        AtomicReference<ResponseException> recreationException = new AtomicReference<>();
        Runnable deleteJob = () -> {
            boolean forceDelete = randomBoolean();
            try {
                String url = MachineLearning.BASE_PATH + "anomaly_detectors/" + jobId;
                if (forceDelete) {
                    url += "?force=true";
                }
                Response response = client().performRequest(new Request("DELETE", url));
                responses.put(Thread.currentThread().getId(), response);
            } catch (ResponseException re) {
                responseExceptions.put(Thread.currentThread().getId(), re);
            } catch (IOException e) {
                ioe.set(e);