Do we need factor rotation? Of all the factors? Do the strongest unrotated factor reveal the "general factor"?
Books do not urge, "rotate, don't leave your factors unrotated". Rather, they say that rotation can benefit in interpretation.
Factor rotations are done for the sake of more easy and "better" interpretation of the meaning of factors (the latent features). One is absulutely free in how to rotate their factors if to rotate at all. Rotation does not change juxtaposition of the variable vectors in the space of loadings, but only coordinates thereof.
In particular, you may rotate not all the factors (i.e. not the entire post-extraction loading matrix columns), but only selected factors (selected loading matrix columns). The factors that don't participate in the rotation retain their pre-rotation loadings, as well as their factor or component scores (at least as computed by the regression method). The initial orthogonality of the factors abstained from rotation with the factors undergone rotation is preserved. (In fact, imagine a 3D loading plot with factors - the axes - F1, F2, F3. You can rotate the F2-F3 plane orthogonal to F1 around the F1 axis. You can even bring close or move apart the F2 and F3 axes, making the rotation of F2-F3 subsystem an oblique one, - but F1 remains fixed and orthogonal to them both, and loadings for F1 won't change.)
So, if you want to preserve an extracted factor as it is, such as the first factor which you consider to be enough "general" factor, then just don't touch it, and rotate all the other factors towards some "simple structure" facilitating their interpretation. Another possible approach might be first to perform quartimax rotation on all the factors (quartimax may aid for further "generalization" of the most strong, i.e. 1st, extracted factor), and only then to rotate the rest of the factors by varimax or other method.
Does "general factor" exist? This is a philosophical question (and also connected with this one). Many researchers don't admit the factor, and when they do they may differ in a theoretical concept of it. Some proponents of the general factor may demand, for example, first to do PCA to skim the 1st component away from the data (or from correlation matrix), and then turn to do FA of the residual data/correlations. This approach is not unreasonable, because the general factor (embodied in the 1st PC here) is being removed from all the variability before the conception of unique factors (as the variabilities orthogonal to the common factors) is introduced via FA. (Indeed, do variables have to have any uniqueness protected from the general factor of correlatedness? It depends on what you think that factor is.)
Another problematic topic is whether extracted (unrotated) factors uncover "general factor" at all. Methods of factor extraction differ. Some methods may yield the same solution however somewhat rotated differently relative each other. This fact questions it whether unrotated result could be of any value at all and suggests that a rotation is perhaps necessary? Next to remark, PAF method maximizes loadings of the 1st factor, then of the 2nd one, etc. as its primary goal (and so as if fitting ones expectations for the "general" factor), but other methods do not "hunt" this goal. Will they (unrotated) uncover "general factor" and what one? These are not very easy questions.
A bonus question from a comment. "How to get the loading matrix after any method of factor extraction where, in the manner of PAF method, the variance (i.e. sum of squared loadings) of the 1st factor is maximized, the variance of the 2nd factor is next possible maximal, etc?" I mean: variance maximized, not simply that factors are sorted by the amount of their variance. The answer is obvious: just perform PCA of the loading matrix as if it were some "data" and the columns (factors) were the data "variables". But do not center the columns, perform PCA without centering. (That is, apply SVD to raw loadings as they are.) The "PC scores" from this PCA will be the solution you seek.