In a discrete setting, when the sampling space $\mathfrak X^n$ (assuming an iid sample of size $n$ from an exponential family) is finite or countable, the conditional distribution of
$$\mathbb P(\mathbf X_n=\mathbf x_n;\theta\vert T_n(\mathbf X_n)=t_n)= \dfrac{\mathbb P(\mathbf X_n=\mathbf x_n;\theta)}{\mathbb P(T_n(\mathbf X_n)=t_n;\theta)}$$
Hence, using the counting measures on $\mathfrak X^n$ and $T(\mathfrak X^n)$, respectively,
$$f(\mathbf x_n;\theta) = f(\mathbf x_n\vert T_n(\mathbf X_n)=t_n) \times f(t_n;\theta)$$
In the continuous case, the result is somehow a tautology since the choice of the (three) dominating measures is open. To start, we can wlog assume that$$T_n(\mathbf x_n)=\sum_{i=1}^n x_i$$
and that the density with respect to a measure $\lambda^{\otimes n}$ on $\mathfrak X^n$ is
$$f(\mathbf x_n;\theta) = \prod_{i=1}^n h(x_i) \times e^{\theta\cdot T_n(\mathbf x_n) - n A(\theta)}$$
Changing the measure into$$\text d\mu(x) \stackrel{\text{def}}{=} h(x)\,\text d\mu(x)$$leads to the density with respect to the measure $\mu^{\otimes n}$ on $\mathfrak X^n$
$$f(\mathbf x_n;\theta) = e^{\theta\cdot T_n(\mathbf x_n) - n A(\theta)}$$
The joint density of $(T_n(\mathbf X),X_2,\ldots,X_n)$ wrt the measure $\mu_{T_n}\otimes\mu^{\otimes n-1}$ on $T_n(\mathfrak X^n)\times\mathfrak X^{n-1}$ is then
$$f(t_n,x_2,\ldots,x_n;\theta) = e^{\theta\cdot t_n - n A(\theta)}\tag{1}$$
since the Jacobian is equal to one. The marginal density of $T_n(\mathbf X)$ wrt $\mu_{T_n}$ is thus
$$f(t_n;\theta) = e^{\theta\cdot t_n - n A(\theta)}\times \tilde h(t_n)$$
where the last term $\tilde h(t_n)$ is the mass of $$T_n(t_n-\sum_{i\ge 2} x_i,x_2,\ldots,x_n)=t_n\tag{2}$$ under $\mu^{\otimes n-1}$. Changing the measure $\mu_{T_n}$ to the new measure $\xi_{T_n}$ defined by
$$\text d\xi_{T_n} (t)= \tilde h(t)\text d\mu_{T_n}(t)$$
leads to the following density of $T_n(\mathbf X)$ wrt $\xi_{T_n}$:
$$f(t_n;\theta) = e^{\theta\cdot t_n - n A(\theta)}$$
If one defines $\zeta_{t_n}$ as the mass one uniform measure over the slice $$\left\{(x_1,x_2,\ldots,x_n); \ T_n(x_1,x_2,\ldots,x_n)=\sum_{i=1}^n x_i=t_n\right\},$$ the conditional density of $\mathbf X$ conditional on $T_n(\mathbf X)$ is one and
$$f_X\left(\,\mathbf{x}_n;\theta\,\right) = f_{T_n}\!\left(\,t_n; \theta\,\right) f_{X|T=t_n}\!\left(\,\mathbf{x}_n | T_n=t_n\right)$$
when the densities are taken wrt $\mu^{\otimes n-1}$, $\xi_{T_n}$, and $\zeta_{t_n}$, respectively.
(Note: Using $f$ for different densities is an abuse of notation, but avoids an explosion in said notation.)
