## Step 1: Define the symbolic representation of the problem
Let's denote the batches of pancakes as $x_1$ and the batches of pretzels as $x_2$. The profit per batch of pancakes is $25, and per batch of pretzels is $50. The oven time used for pancakes is 2.5 hours and for pretzels is 1.5 hours. The pastry chef time used for pancakes is 0.5 hours and for pretzels is 3 hours. The available oven time is 65 hours, and the available pastry chef time is 35 hours.

## Step 2: Formulate the objective function
The objective function to maximize profit is $25x_1 + 50x_2$.

## 3: Formulate the constraints
The constraints based on the available times are:
1. $2.5x_1 + 1.5x_2 \leq 65$ (oven time constraint)
2. $0.5x_1 + 3x_2 \leq 35$ (pastry chef time constraint)
Also, $x_1 \geq 0$ and $x_2 \geq 0$ since the batches cannot be negative.

## 4: Convert the problem into a symbolic representation
The symbolic representation is:
- Variables: $x_1$ (pancakes), $x_2$ (pretzels)
- Objective function: $25x_1 + 50x_2$
- Constraints:
  - $2.5x_1 + 1.5x_2 \leq 65$
  - $0.5x_1 + 3x_2 \leq 35$
  - $x_1 \geq 0$
  - $x_2 \geq 0$

## 5: Express the problem in the required JSON format
```json
{
'sym_variables': [('x1', 'pancakes'), ('x2', 'pretzels')],
'objective_function': '25*x1 + 50*x2',
'constraints': [
  '2.5*x1 + 1.5*x2 <= 65',
  '0.5*x1 + 3*x2 <= 35',
  'x1 >= 0',
  'x2 >= 0'
]
}
```

## 6: Implement the problem in Gurobi code
```python
import gurobi

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

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

    # Define the objective function
    model.setObjective(25*x1 + 50*x2, gurobi.GRB.MAXIMIZE)

    # Define the constraints
    model.addConstr(2.5*x1 + 1.5*x2 <= 65, name="oven_constraint")
    model.addConstr(0.5*x1 + 3*x2 <= 35, name="pastry_chef_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Pancakes: {x1.varValue}")
        print(f"Pretzels: {x2.varValue}")
        print(f"Maximum profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_bakery_problem()
```