To solve this optimization problem, we first need to define the decision variables, objective function, and constraints based on the given information.

Let's denote:
- \(B\) as the number of burritos sold,
- \(T\) as the number of tacos sold.

The profit per burrito is $3, and the profit per taco is $3.50. Thus, the total profit (objective function) can be represented as:
\[ \text{Maximize:} \quad 3B + 3.5T \]

Given the constraints:
- Each burrito contains 4 units of beef, and each taco contains 4.5 units of beef. The restaurant has 500 units of beef available.
- Each burrito contains 4 units of toppings, and each taco contains 3 units of toppings. The restaurant has 400 units of toppings available.

We can represent these constraints as:
\[ 4B + 4.5T \leq 500 \] (beef constraint)
\[ 4B + 3T \leq 400 \] (toppings constraint)

Also, the number of burritos and tacos sold cannot be negative:
\[ B \geq 0 \]
\[ T \geq 0 \]

Now, let's translate this into Gurobi code in Python:

```python
from gurobipy import *

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

# Define the decision variables
B = m.addVar(vtype=GRB.CONTINUOUS, name="burritos", lb=0)
T = m.addVar(vtype=GRB.CONTINUOUS, name="tacos", lb=0)

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

# Add constraints
m.addConstr(4*B + 4.5*T <= 500, "beef_constraint")
m.addConstr(4*B + 3*T <= 400, "toppings_constraint")

# Optimize the model
m.optimize()

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