Without having sample data this is a bit like detective work.
The missing values mentioned in the log can be explained as follows:
The six variables mid_Grd7-mid_Grd12 are all missing for the 459 observations from dataset IMPUTE_HIGH_SCHOOLS. Most probably they are simply not contained in that dataset.
The six variables hs_Grd7-hs_Grd12 are all missing for the 610 observations from dataset IMPUTE_MID_SCHOOLS. Again, most probably they are not contained in that dataset.
Certainly, none of the above 12 variables is contained in dataset TOTALTEACHERS (which contains only School, County and Teachers) with its 1069 observations.
That's why you get (in "line 74" of the log):
459+1069=1528 missing values for sum(of mid_Grd7-mid_Grd12)
610+1069=1679 missing values for sum(of hs_Grd7-hs_Grd12)
459+610+1069=2138 missing values, i.e. only missing values, for variable Total_Students_grd7togrd12.
This last fact in turn explains the 2138 missing values you obtain for variable PTRatio_rev in "line 76" (by dividing missing values by values of variable Teachers, many of which are most likely also missing, because only dataset TOTALTEACHERS can be reasonably expected to contain this variable according to your first data step).
So, merely concatenating the three datasets with the SET statement does not create a suitable basis for your calculations. Here is an instructive example of what happened in your program: Using the SET Statement When Data Sets Contain Different Variables.
If you need more help, please post a few lines of sample data (for each of the three datasets involved) in the form of a datastep (see instructions) and describe what you want as output.
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