[ML] Implement new rules design (#31110) (#31294)

Rules allow users to supply a detector with domain
knowledge that can improve the quality of the results.
The model detects statistically anomalous results but it
has no knowledge of the meaning of the values being modelled.

For example, a detector that performs a population analysis
over IP addresses could benefit from a list of IP addresses
that the user knows to be safe. Then anomalous results for
those IP addresses will not be created and will not affect
the quantiles either.

Another example would be a detector looking for anomalies
in the median value of CPU utilization. A user might want
to inform the detector that any results where the actual
value is less than 5 is not interesting.

This commit introduces a `custom_rules` field to the `Detector`.
A detector may have multiple rules which are combined with `or`.

A rule has 3 fields: `actions`, `scope` and `conditions`.

Actions is a list of what should happen when the rule applies.
The current options include `skip_result` and `skip_model_update`.
The default value for `actions` is the `skip_result` action.

Scope is optional and allows for applying filters on any of the
partition/over/by field. When not defined the rule applies to
all series. The `filter_id` needs to be specified to match the id
of the filter to be used. Optionally, the `filter_type` can be specified
as either `include` (default) or `exclude`. When set to `include`
the rule applies to entities that are in the filter. When set to
`exclude` the rule only applies to entities not in the filter.

There may be zero or more conditions. A condition requires `applies_to`,
`operator` and `value` to be specified. The `applies_to` value can be
either `actual`, `typical` or `diff_from_typical` and it specifies
the numerical value to which the condition applies. The `operator`
(`lt`, `lte`, `gt`, `gte`) and `value` complete the definition.
Conditions are combined with `and` and allow to specify numerical
conditions for when a rule applies.

A rule must either have a scope or one or more conditions. Finally,
a rule with scope and conditions applies when all of them apply.

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/calendars/ScheduledEvent.java

@@ -16,7 +16,6 @@
 import org.elasticsearch.common.xcontent.XContentBuilder;
 import org.elasticsearch.common.xcontent.XContentParser;
 import org.elasticsearch.xpack.core.ml.MlMetaIndex;
-import org.elasticsearch.xpack.core.ml.job.config.Connective;
 import org.elasticsearch.xpack.core.ml.job.config.DetectionRule;
 import org.elasticsearch.xpack.core.ml.job.config.Operator;
 import org.elasticsearch.xpack.core.ml.job.config.RuleAction;
@@ -148,8 +147,7 @@ public DetectionRule toDetectionRule(TimeValue bucketSpan) {
         conditions.add(RuleCondition.createTime(Operator.LT, bucketEndTime));
 
         DetectionRule.Builder builder = new DetectionRule.Builder(conditions);
-        builder.setActions(RuleAction.FILTER_RESULTS, RuleAction.SKIP_SAMPLING);
-        builder.setConditionsConnective(Connective.AND);
+        builder.setActions(RuleAction.SKIP_RESULT, RuleAction.SKIP_MODEL_UPDATE);
         return builder.build();
     }