AutoScaling: Mixed instance autoscaling groups

[viren-nadkarni]

Please see the comment below for PR description

[baermat]

This PR adds more extensive calculations for MixedInstanceTypes for auto scaling groups, which are closer to what AWS does internally.
When a mixed instance types template is passed, the following should happen:

• The base should be filled with on-demand instances
• The remaining capacity up to desired should be filled with either on-demand or spot instances
  • This is determined by the passed percentage
• All of this is measured in abstract units, not in instances. Instances are assigned a certain unit value, which can lead to "extra ceil rounding instances" . If e.g. an instances has weight 4 units, and desired capacity is 8, but the calculation calls for 2u + 3u + 3u, it results in 3 instances, not 2 4u+4u (see test for details).

Limitations: The function currently expects a "prioritized" strategy for the OnDemandAllocationStrategy. Adding more strategies would have been out of scope, so this was left as is.

[baermat]

@viren-nadkarni please start the workflows, review the changes and if ready mark as ready for review please :)

[codecov]

Codecov Report

❌ Patch coverage is 96.87500% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 93.04%. Comparing base (0d4bc17) to head (1734892).
⚠️ Report is 38 commits behind head on master.

Files with missing lines	Patch %	Lines
moto/autoscaling/models.py	96.87%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #9444      +/-   ##
==========================================
+ Coverage   93.02%   93.04%   +0.01%     
==========================================
  Files        1292     1295       +3     
  Lines      116184   116441     +257     
==========================================
+ Hits       108083   108341     +258     
+ Misses       8101     8100       -1     

Flag	Coverage Δ
servertests	28.89% <3.12%> (+0.04%)	⬆️
unittests	93.01% <96.87%> (+0.01%)	⬆️

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[bblommers]

LGTM - thanks for adding this feature to Moto @baermat and @viren-nadkarni!