To solve this problem, we first need to translate the natural language description into a symbolic representation. Let's define the variables as follows:

* $x_1$: number of batches of donuts
* $x_2$: number of batches of cookies

The objective function is to maximize profit, which can be represented algebraically as:

$20x_1 + 15x_2$

The constraints are:

* Jake's time constraint: $20x_1 + 5x_2 \leq 200$
* Jill's time constraint: $10x_1 + 25x_2 \leq 300$
* Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

The symbolic representation of the problem is:

```json
{
    'sym_variables': [('x1', 'number of batches of donuts'), ('x2', 'number of batches of cookies')],
    'objective_function': '20*x1 + 15*x2',
    'constraints': ['20*x1 + 5*x2 <= 200', '10*x1 + 25*x2 <= 300', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's write the Gurobi code to solve this problem:

```python
from gurobipy import *

# Create a model
m = Model("Bakery_Optimization")

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

# Set objective function
m.setObjective(20*x1 + 15*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(20*x1 + 5*x2 <= 200, "jake_time")
m.addConstr(10*x1 + 25*x2 <= 300, "jill_time")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Donuts: {x1.x}")
    print(f"Cookies: {x2.x}")
    print(f"Max Profit: {m.objVal}")
else:
    print("No optimal solution found")
```