By definition, a loading is the amount of variability shared by variables due to the common factor. 100% of the common varaince of the pair of variables is explained by the common factor(s).
By definition of common factor analysis, it aims to explain 100% of correlation by the common latent factor(s). FA assumes pairwise partial correlations are negligible. Either they are zero or they are small and constitute those "rubbish" common factors which we do not bother to model.
Check "Population noise added" here and read this.
Factor analysis output displays the reproduced (by the factors) correlations. You can always compare it with the input correlations. Ideally, the two matrices should be close or almost identical. If they are not, consider extracting more factors (going along the road of overfitting). But do not expect FA will explain (model) pairwise partial associations for you. FA assumes they are neglibible enough not to be modelled, and if the aren't small - then the data are not well suited for FA.
Distinction between "shared variance" and "common variance". Factor analysis explains shared variance in pairs of variables by the common variance they are invested by the factor. The variables are denied having their private (partial) share, instead, that load from the factor is what makes them covariate, by the amount by which they get loaded.
Partial, i.e. naturally two-party correlations, are almost nonsense theoretically. Take 100 correlating variables. Do you think there truly exist, in population, 100(100-1)/2 special factors to constitute/regulate their relationship? I think no. Rather, there exist a small number of common factors (say, up to 10) which force the 100 items to correlate. And to the extent they don't correlate with r=1, they vary individually for that rest. If, above all that, they still uniquely co-vary in pairs, then this could be only for few pairs and only weakly. That is a reasonable view embodied in FA.