I’ll say that a model linearly represents a binary feature f if there is a linear probe out of the model’s latent space which is accurate for classifying f

If a model linearly represents features a and b, then it automatically linearly represents $a\wedge b$ and $a\vee b$.

I think I misunderstand your definition. Let feature a be represented by x_1 > 0.5, and let feature b be represented by x_2 > 0.5. Let x_i be iid uniform [0, 1]. Isn't that a counterexample to (a and b) being linearly representable?