## Step 1: Define the symbolic variables and the objective function
Let's define the symbolic variables and the objective function based on the given problem description.

- $x_1$ represents the number of muffins.
- $x_2$ represents the number of milk cakes.

The objective function to maximize profit is: $6.5x_1 + 8.5x_2$.

## Step 2: Define the constraints
Based on the problem description, we have the following constraints:

1. Milk availability constraint: $15x_1 + 100x_2 \leq 25000$.
2. Muffin to milk cake ratio constraint: $x_2 \geq 2x_1$.
3. Minimum muffin production constraint: $x_1 \geq 50$.
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$.

However, since $x_1$ and $x_2$ represent quantities of products, we can reasonably assume $x_1 \geq 0$ and $x_2 \geq 0$ are implicitly satisfied by the other constraints.

## 3: Symbolic Representation
The symbolic representation of the problem is:

```json
{
'sym_variables': [('x1', 'muffins'), ('x2', 'milk cakes')],
'objective_function': '6.5*x1 + 8.5*x2',
'constraints': [
    '15*x1 + 100*x2 <= 25000',
    'x2 >= 2*x1',
    'x1 >= 50'
]
}
```

## 4: Gurobi Code
Now, let's write the Gurobi code in Python to solve this optimization problem.

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(lb=0, name="muffins")
    x2 = model.addVar(lb=0, name="milk_cakes")

    # Set the objective function
    model.setObjective(6.5 * x1 + 8.5 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(15 * x1 + 100 * x2 <= 25000, name="milk_availability")
    model.addConstr(x2 >= 2 * x1, name="muffin_to_milk_cake_ratio")
    model.addConstr(x1 >= 50, name="minimum_muffin_production")

    # Optimize the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of muffins: {x1.varValue}")
        print(f"Number of milk cakes: {x2.varValue}")
        print(f"Maximum profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```