First of all, there are four kinds of "Fourier transforms" in common use, so the cardinality problem that you have observed cannot really be discussed until we pick one to talk about. For instance, the FT maps uncountable to uncountable and the DFT maps finite to finite (and can be understood with intro Linear Algebra as a change of basis). The things we are calling countable and uncountable are actually dimensionalities of vector spaces, since real multiples of a single function would produce an uncountable set. Anyway, here are the types of transforms:

D=continuous domain, d=discrete domain, E=infinite extent, e=finite extent

time domain <-> frequency domain

Fourier Transform: DE<->DE

Fourier Series: De<->dE

Discrete Time Fourier Transform: dE<->De

Discrete Fourier Transform: de<->de

The cardinality mismatch shows up for FS and DTFT. It is resolved through a different notion of equality than you may be used to. The "distance" between two functions can be measured using several norms, and when this distance is zero we claim two functions are equal. L1, L2, and Linf are the common ones. Linf corresponds to pointwise equality, which is what you assumed (not unreasonably) that they meant. L1 and L2 do not.

L1 norm: d(f,g)=integrate(abs(f(x)-g(x)),a,b)

L2 norm: d(f,g)^2=integrate(abs(f(x)-g(x))^2,a,b)

Linf norm: d(f,g)=max(f(x)-g(x)) over the interval (a,b)

Fourier Series only guarantee reproduction (take the transform and then take the inverse to get the reproduced curve) up to the L2 norm. This makes physicists and engineers happy, since L2 norms correspond to energy measurements, and if the difference between two physical quantities has no energy then it's effectively irrelevant to physical processes.

The remaining hurdles for mathematicians are to show that

1) The reproduction coefficients found by the transform are the best possible coefficients (the curve they generate is closer to the original than for any other set of coefficients of the same size). This is a trivial proof using inner products that usually goes by a name like "best approximation theorem" or "generalized pythagorean theorem."

2) Reproducible curves are dense in the space of actual curves. That is, there is always a reproducible curve that has zero L2 difference from an arbitrary input curve. If this is true, then by #1 we know that the transforms will find it. Unfortunately, this proof is quite involved. A "quick and dirty" method uses the Stone-Wierstrass theorem (polynomials can reproduce continuous functions with arbitrary L1 or L2 fidelity + the FS can reproduce polynomials with arbitrary L2 fidelity). A better method arises in the context of Sturm-Liouville theory that generalizes "density" to solutions of a large class of differential equations. This is important for physicists and engineers because it justifies normal mode expansions even when the normal modes aren't perfect sinusoids.

If you want to know more, any linear algebra book that discusses inner products should hit on #1. For #2 you want an Analysis textbook. Analysis is a tough subject so it's far more important to be sure that the book starts at your level and has an understandable proof of Stone-Wierstrass than it is to be sure that it covers this exact topic. If it doesn't, supplement it with the first few chapters from "Completeness and Basis Properties of Sets of Special Functions" by Higgins and you'll be set.