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

Let's define:
- $x_1$ as the number of burritos sold.
- $x_2$ as the number of tacos sold.

The profit per burrito is $3, and the profit per taco is $3.50. Therefore, the objective function to maximize profit can be represented as:
\[ \text{Maximize:} \quad 3x_1 + 3.5x_2 \]

Given that each burrito contains 4 units of beef and each taco contains 4.5 units of beef, and the restaurant has 500 units of beef available, we have the first constraint:
\[ 4x_1 + 4.5x_2 \leq 500 \]

For the toppings, with each burrito containing 4 units and each taco containing 3 units, and 400 units of toppings available, the second constraint is:
\[ 4x_1 + 3x_2 \leq 400 \]

Additionally, since we cannot sell a negative number of burritos or tacos, we have non-negativity constraints:
\[ x_1 \geq 0 \]
\[ x_2 \geq 0 \]

Thus, the symbolic representation of our problem is:

```json
{
    'sym_variables': [('x1', 'number of burritos'), ('x2', 'number of tacos')],
    'objective_function': '3*x1 + 3.5*x2',
    'constraints': ['4*x1 + 4.5*x2 <= 500', '4*x1 + 3*x2 <= 400', 'x1 >= 0', 'x2 >= 0']
}
```

Now, to solve this problem using Gurobi in Python:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="burritos")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="tacos")

# Set the objective function
m.setObjective(3*x1 + 3.5*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(4*x1 + 4.5*x2 <= 500, "beef_limit")
m.addConstr(4*x1 + 3*x2 <= 400, "toppings_limit")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Burritos to sell: {x1.x}")
    print(f"Tacos to sell: {x2.x}")
    print(f"Maximum profit: ${3*x1.x + 3.5*x2.x:.2f}")
else:
    print("No optimal solution found")
```