### 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/src/main/java/org/elasticsearch/xpack/ml/action/TransportStartDatafeedAction.java
transportService.getRemoteClusterService().getRegisteredRemoteClusterNames(),
            remoteIndices
        ).size();
        assert numberOfRemoteClusters > 0;
        final String remoteClusterQualifier = numberOfRemoteClusters == 1 ? "a remote cluster" : "remote clusters";
        final String licenseTypeQualifier = numberOfRemoteClusters == 1 ? "" : "s";
        final String message = String.format(
            Locale.ROOT,
            "cannot start datafeed [%s] as it uses indices on %s %s but the license type%s could not be verified",
            datafeedId,
            remoteClusterQualifier,
            remoteIndices,
            licenseTypeQualifier
        );
        return new ElasticsearchStatusException(message, RestStatus.BAD_REQUEST, cause);
    }
    public static class StartDatafeedPersistentTasksExecutor extends PersistentTasksExecutor<StartDatafeedAction.DatafeedParams> {
        private final DatafeedRunner datafeedRunner;
        private final IndexNameExpressionResolver resolver;
        public StartDatafeedPersistentTasksExecutor(DatafeedRunner datafeedRunner, IndexNameExpressionResolver resolver) {
            super(MlTasks.DATAFEED_TASK_NAME, MachineLearning.UTILITY_THREAD_POOL_NAME);
            this.datafeedRunner = datafeedRunner;
            this.resolver = resolver;
        }
        @Override
        public PersistentTasksCustomMetadata.Assignment getAssignment(
            StartDatafeedAction.DatafeedParams params,
            Collection<DiscoveryNode> candidateNodes,
            ClusterState clusterState
        ) {