You are technically correct that based on your conditions you have no minimum. But there is a function which minimizes your integral if you ignore the condition that $f'(0)=0$. The problem is that you have basically solved the ODE
$$\frac{\partial L}{\partial f}-\frac{d}{dx}\frac{\partial L}{\partial f'}$$
where $L=f^2+f'^2$ are your initial conditions. Of course this will only give you a stationary solution. However, your mistake is trying to apply the condition $f'(0)=0$ when it actually does not matter. Observe: let $g(x)$ be any function such that $g(0)=1$ and let $h(x)$ be the cubic function such that $h(0)=1$, $h'(0)=0$, $h(\epsilon)=g(\epsilon)$, and $h'(\epsilon)=g'(\epsilon)$. Then the function defined
$$f(x)=\left\{\begin{array}{ll}
h(x) & x\in[0,\epsilon) \\
g(x) & x\in [\epsilon,\infty) \\
\end{array}
\right.
$$
clearly satisfies the conditions in the question but
$$\lim_{\epsilon\to 0^+} \int_0^T f'(x)^2+f(x)^2=\int_0^T g'(x)^2+g(x)^2$$
since
$$0\leq \int_0^\epsilon h'(x)^2+h(x)^2\leq \epsilon \cdot\text{max}_{x\in [0,1]}[ h'(x)^2+h(x)^2]$$
To properly show this last statement it would be necessary to actually write out and define the cubic $h(x)$ but that is not too hard with a good computer software tool.
But now we can actually solve your problem: solving the functional
$$\left[\frac{\partial}{\partial f}-\frac{d}{dx}\frac{\partial}{\partial f'}\right](f^2+f'^2)=0$$
with the conditions $f(0)=1$ and $f(T)=s$. We will solve for $s$ later. Now, using these conditions we get the equation
$$f(x)=(1-b)e^x+be^{-x}$$
$$b=\frac{e^T-s}{e^T-e^{-T}}$$
The integral isn't too difficult, and we get that
$$\int_0^T f(x)^2+f'(x)^2=(1 + s^2) \coth(T) - 2 s \text{csch}(T)$$
At this point, we can find $s$ as well by normal calculus methods (differentiate and set to $0$). Solving gives
$$s=\text{sech}(T)$$
for a final minimum solution of
$$f(x)=(1-b)e^x+be^{-x}$$
$$b=\frac{e^T-\text{sech}(T)}{e^T-e^{-T}}$$
Of course, as you noted this function does not satisfy $f'(0)=0$ and can only be thought of as a global infimum in this minimization problem.