[ML] lat_long anomaly not detected anymore

#### Summary

We used to have a job configuration that produced an anomaly and now it doesn't do that anymore, so we'd like to assess whether or not this is an expected change / a change we want to tolerate.

### Steps to reproduce

1. Install the Kibana ecommerce sample data
2. Create and run the anomaly detection lookback job (synchronize Kibana saved objects if needed)

Config

```
PUT _ml/anomaly_detectors/ecommerce-geo
{
"analysis_config" : {
"bucket_span":"15m",
"detectors": [
{
"detector_description": "Unusual coordinates by user",
"function": "lat_long",
"field_name": "geoip.location",
"by_field_name": "user"
}
],
"influencers": [
"geoip.country_iso_code",
"day_of_week",
"category.keyword",
"user"
]
},
"data_description" : {
"time_field": "order_date"
},
"datafeed_config":{
"datafeed_id": "datafeed-ecommerce-geo",
"indices": ["kibana_sample_data_ecommerce"],
"query": {
"bool": {
"must": [
{
"match_all": {}
}
]
}
}
}
}
```

3. View the job results

### Additional information

- This job config was used to create screenshots for the docs, e.g. the first one in [this section](https://www.elastic.co/guide/en/machine-learning/master/geographic-anomalies.html#geographic-anomalies-results):
- When looking at the data for `user: jackson`, we can see 114 documents with 113 of them having the same `geoip.location` somewhere near Los Angeles and one of them having a `geoip.location` near New York
- We can also see this by running a high precision geohash grid aggregation:
```
GET kibana_sample_data_ecommerce/_search
{
"query": {
"simple_query_string": {
"query": "jackson",
"fields": ["user"]
}
},
"aggs": {
"locations": {
"geohash_grid": {"field": "geoip.location", "precision": 12}
}
},
"size": 0
}
```
which gives us
```
[...]
"locations" : {
"buckets" : [
{
"key" : "9q5cyr9qukez",
"doc_count" : 113
},
{
"key" : "dr5rs14yejbs",
"doc_count" : 1
}
]
}
```
- So from this data, the originally detected anomaly seems correct.

Beginne mit der Installation der Kibana-E-Commerce-Beispieldaten und führe die dokumentierte Konfiguration des ecommerce-geo-Anomaliedetektors aus. Untersuche die Ergebnisse für den Benutzer jackson und vergleiche sie mit der bereitgestellten Geohash-Aggregation, die einen Standort in New York zwischen Punkten aus Los Angeles zeigt. Als erledigt gilt, wenn festgestellt wurde, ob die fehlende Anomalie eine erwartete Änderung oder eine Regression ist und die erforderliche Folgemaßnahme identifiziert wurde.

Vom Indexierungsmodell aus dem Issue-Text verfasst.