First, some background, in which I'll ignore subtleties and just address the main ideas. Solving the eigenvalue equation for a normal operator (of which self-adjoint operators are a special case) is essentially just a route to the so-called spectral decomposition of the operator, which takes the form
$$\hat A = \sum_n \lambda_n \hat P_n$$
where $\lambda_n$ is the $n^{th}$ eigenvalue of $\hat A$ and $\hat P_n$ is the orthogonal projection operator which acts on a vector by projecting it into the eigenspace corresponding to $\lambda_n$. Assuming that $\lambda_n$ is non-degenerate, $\hat P_n$ takes the familiar form $|\phi_n\rangle\langle \phi_n|$ where $|\phi_n\rangle$ is the (normalized) eigenvector with eigenvalue $\lambda_n$.
This decomposition is important for a number of reasons. First, if $\hat A$ is a self-adjoint operator representing some observable, then the $\mathbb R$-valued eigenvalues correspond to possible measurement outcomes. If a system is in a pure state with (normalized) state vector $|\psi\rangle$, then the probability of measuring $\hat A$ to take the value $\lambda_n$ is given by $\langle \psi|\hat P_n |\psi\rangle$, and the (unnormalized) post-measurement state is given by $\hat P_n|\psi\rangle$. Therefore, the spectral decomposition of an operator allows you to compute the probability of all possible measurement outcomes, as well as the post-measurement state obtained in each case.
The decomposition is also important if we wish to compute a function of the operator. Given an ordinary function $f:\mathbb C\rightarrow \mathbb C$, we can define the operator $f(\hat A)$ as
$$f(\hat A) = \sum_n f(\lambda_n) \hat P_n$$
Of particular importance is the time evolution operator which tells us how to evolve our state vectors forward in time; for a time-independent Hamiltonian $\hat H$, the time-evolution operator is given by
$$\hat U(t) = \exp\big[-i\hat H t/\hbar\big] = \sum_n e^{-iE_nt/\hbar} \hat P_n$$
Other examples include the rotation operators, which are obtained by exponentiating the angular momentum operators, and inverse operators of the (formal) form $\hat A^{-1} \equiv 1/\hat A$ which are of great utility in e.g. perturbation theory.
But what doesn't make sense is, if an operator always return scalar values, then isn't it just a scalar function in the first place and so wouldn't even have eigenvectors and eigenvalues?
When people say that operating on an eigenvector turns the corresponding eigenvalue, what they mean is that the eigenvalue can be determined simply by looking at the resulting expression. The operator $-\frac{d^2}{dx^2}$ acting on $\sin(2x)$ yields
$$\underbrace{-\frac{d^2}{dx^2}}_{\text{operator}} \underbrace{\sin(2x)}_\text{vector} = \underbrace{4}_\text{eigenvalue} \underbrace{\sin(2x)}_{\text{vector}}$$
In other words, the operator does not literally return $4$ as an output, but by acting on the vector with the operator we can read off the eigenvalue from the result.
Or, perhaps a better way to put it is, if only the eigenvalues of the wavefunction correspond to something observable, why do we care about the rest of the wavefunction? Like, what useful information, even just as an intermediate step in a calculation to allow us to do the rest of the calculation, do we get from applying an operator to a part of a wavefunction that isn't an eigenfunction?
I'm not really sure what to make of this question. The last part is answered in part by my background section above; there are many operators (e.g. symmetry transformations like rotation operators or translation operators, or the time evolution operator) which are interesting and useful because of the transformation they impose on the state vector of the system, not because they correspond to observables.