To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the problem statement.

Let's define:
- $x_1$ as the number of chocolate donuts made,
- $x_2$ as the number of maple donuts made.

The objective function aims to maximize profit. Given that the profit per chocolate donut is $2 and per maple donut is $3, the objective function can be represented as:
\[ \text{Maximize: } 2x_1 + 3x_2 \]

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. At most 100 chocolate donuts can be made: $x_1 \leq 100$
4. At most 150 maple donuts can be made: $x_2 \leq 150$
5. Total of at least 200 donuts must be made per day: $x_1 + x_2 \geq 200$

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'number of chocolate donuts'), ('x2', 'number of maple donuts')],
    'objective_function': 'Maximize: 2*x1 + 3*x2',
    'constraints': ['x1 >= 50', 'x2 >= 75', 'x1 <= 100', 'x2 <= 150', 'x1 + x2 >= 200']
}
```

To solve this problem using Gurobi in Python, we can use the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, ub=100, vtype=GRB.INTEGER, name="chocolate_donuts")
x2 = m.addVar(lb=0, ub=150, vtype=GRB.INTEGER, name="maple_donuts")

# Set the objective function
m.setObjective(2*x1 + 3*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 >= 50, "min_chocolate")
m.addConstr(x2 >= 75, "min_maple")
m.addConstr(x1 <= 100, "max_chocolate")
m.addConstr(x2 <= 150, "max_maple")
m.addConstr(x1 + x2 >= 200, "total_donuts")

# Optimize the model
m.optimize()

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