To solve this problem, we first need to convert the natural language description into a symbolic representation. Let's denote:

- $x_1$ as the number of muffins to be made,
- $x_2$ as the number of milk cakes to be made.

The objective function aims to maximize profit. Each muffin is sold for a profit of $6.5, and each milk cake is sold for a profit of $8.5. Thus, the objective function can be represented algebraically as:

\[ \text{Maximize:} \quad 6.5x_1 + 8.5x_2 \]

The constraints are:
1. At least twice the amount of milk cakes are needed than muffins: \( x_2 \geq 2x_1 \)
2. There need to be at least 50 muffins made: \( x_1 \geq 50 \)
3. Each muffin needs 15 grams of milk, and each milk cake needs 100 grams of milk, with a total of 25,000 grams available: \( 15x_1 + 100x_2 \leq 25000 \)
4. Non-negativity constraints (since the number of items cannot be negative): \( x_1 \geq 0, x_2 \geq 0 \)

Given this symbolic representation, we have:

```json
{
    'sym_variables': [('x1', 'number of muffins'), ('x2', 'number of milk cakes')], 
    'objective_function': '6.5*x1 + 8.5*x2', 
    'constraints': ['x2 >= 2*x1', 'x1 >= 50', '15*x1 + 100*x2 <= 25000', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this linear programming problem using Gurobi in Python, we'll write the following code:

```python
from gurobipy import *

# Create a new model
m = Model("CaMilk_Optimization")

# Define variables
x1 = m.addVar(name="muffins", vtype=GRB.INTEGER, lb=0)
x2 = m.addVar(name="milk_cakes", vtype=GRB.INTEGER, lb=0)

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

# Add constraints
m.addConstr(x2 >= 2*x1, name="at_least_twice_as_many_milk_cakes")
m.addConstr(x1 >= 50, name="at_least_50_muffins")
m.addConstr(15*x1 + 100*x2 <= 25000, name="total_milk_limit")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
    print(f"Maximum profit: ${m.objVal:.2f}")
else:
    print("No optimal solution found")
```