[ML] More accurate job memory overhead

[droberts195]

When an ML job runs the memory required can be
broken down into:

1. Memory required to load the executable code
2. Instrumented model memory
3. Other memory used by the job's main process or
   ancilliary processes that is not instrumented

Previously we added a simple fixed overhead to
account for 1 and 3. This was 100MB for anomaly
detection jobs (large because of the completely
uninstrumented categorization function and
normalize process), and 20MB for data frame
analytics jobs.

However, this was an oversimplification because
the executable code only needs to be loaded once
per machine. Also the 100MB overhead for anomaly
detection jobs was probably too high in most cases
because categorization and normalization don't use
that much memory.

This PR therefore changes the calculation of memory
requirements as follows:

1. A per-node overhead of 30MB for only the first
   job of any type to be run on a given node - this
   is to account for loading the executable code
2. The established model memory (if applicable) or
   model memory limit of the job
3. A per-job overhead of 10MB for anomaly detection
   jobs and 5MB for data frame analytics jobs, to
   account for the uninstrumented memory usage

This change will enable more jobs to be run on the
same node. It will be particularly beneficial when
there are a large number of small jobs. It will
have less of an effect when there are a small number
of large jobs.

[elasticmachine]

Pinging @elastic/ml-core (:ml)