## Symbolic Representation

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

- $x_1$ = Number of chocolate donuts
- $x_2$ = Number of maple donuts

The objective is to maximize profit, given that the profit per chocolate donut is $2 and the profit per maple donut is $3. Therefore, the objective function can be represented as:

Maximize: $2x_1 + 3x_2$

The constraints based on the problem description are:

1. Daily demand of at least 50 chocolate donuts: $x_1 \geq 50$
2. Daily demand of at least 75 maple donuts: $x_2 \geq 75$
3. The bakery can make at most 100 chocolate donuts: $x_1 \leq 100$
4. The bakery can make at most 150 maple donuts: $x_2 \leq 150$
5. Must make at least 200 donuts total: $x_1 + x_2 \geq 200$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'chocolate donuts'), ('x2', 'maple donuts')],
    'objective_function': '2*x1 + 3*x2',
    'constraints': [
        'x1 >= 50',
        'x2 >= 75',
        'x1 <= 100',
        'x2 <= 150',
        'x1 + x2 >= 200'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Donut_Optimization")

# Define the variables
x1 = model.addVar(lb=0, name="chocolate_donuts", vtype=gp.GRB.INTEGER)
x2 = model.addVar(lb=0, name="maple_donuts", vtype=gp.GRB.INTEGER)

# Objective function: Maximize 2*x1 + 3*x2
model.setObjective(2*x1 + 3*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 >= 50, name="min_chocolate_demand")
model.addConstr(x2 >= 75, name="min_maple_demand")
model.addConstr(x1 <= 100, name="max_chocolate_production")
model.addConstr(x2 <= 150, name="max_maple_production")
model.addConstr(x1 + x2 >= 200, name="total_donuts")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: Chocolate donuts = {x1.varValue}, Maple donuts = {x2.varValue}")
    print(f"Maximum profit: ${2*x1.varValue + 3*x2.varValue}")
else:
    print("No optimal solution found")
```