Note that the function
$$
\langle A,B \rangle = Tr(A^TB)
$$
defines an inner product over the real matrices. As such, $\|A\|_2^2 = Tr(A^TA)$ is the norm induced by this inner product. We have
$$
Tr((I-A)B + AC) = Tr((I-A)B) + Tr(AC) =\\
\langle I-A,B \rangle + \langle A,C \rangle \leq\\
\|I - A\|\,\|B\| + \|A\|\,\|C\| = \\
\sqrt{Tr[(I - A)^2]Tr(B^2) + Tr(A^2)Tr(C^2)}
$$
We can do a bit better using the inequality $|\langle A,B \rangle| \leq \sigma_1(A)\sum \sigma_i(B)$. In the symmetric positive semidefinite case, amounts to $|\langle A,B \rangle| \leq \rho(A) Tr(B)$, where $\rho$ denotes the spectral radius. We now have
$$
\langle I-A,B \rangle + \langle A,C \rangle \leq\\
\rho(I - A)Tr(B) + \rho(A) Tr(C)
$$
This reduces to your result in the case that $A = aI$ for $a \in [0,1]$.
The reverse inequality works perfectly well for our purposes: note that $A \succeq \lambda_{min}(A)I$. So, $C^{1/2}AC^{1/2} \succeq \lambda_{min}(A)C^{1/2}C^{1/2}$. We therefore have
$$
Tr((I-A)B + AC) = Tr((I-A)B) + Tr(AC) =\\
Tr(B^{1/2}(I-A)B^{1/2}) + Tr(C^{1/2}AC) \geq\\
Tr(\lambda_{min}(I - A)B^{1/2}B^{1/2}) + Tr( \lambda_{min}(A)C^{1/2}C^{1/2}) = \\
\lambda_{min}(I-A)Tr(B) + \lambda_{min}(A) Tr(C)
$$
in retrospect, a similar inequality could have worked in reverse.