RF regressor for probabilites
This is indeed expected behavior, because of the way tree models handle multioutput problems.  The nodes contain some number of samples, and the score for each output is the average of those samples' corresponding output.  Since averaging commutes with sums, the property of summing to 1 is preserved.  I'm not sure if this will help, but in symbols:
$$ \sum_{\text{output }i} p_i = \sum_{\text{output }i} \left(\operatorname*{avg}_{\text{sample }j}(p_i^j)\right) = \operatorname*{avg}_j \left(\sum_{\text{output }i} p_i^j\right) = \operatorname*{avg}_j 1 = 1.$$
Then for the entire forest, you're just applying another averaging, and so the property is again maintained.