To solve the optimization problem described, we first need to identify the decision variables, the objective function, and the constraints.

1. **Decision Variables:**
   - Let \(L\) represent the number of square feet dedicated to growing lettuce.
   - Let \(T\) represent the number of square feet dedicated to growing tomatoes.

2. **Objective Function:**
   - The profit per square foot of lettuce is $2, and the profit per square foot of tomatoes is $3. Therefore, the total profit can be represented as \(2L + 3T\).
   - The objective is to maximize this profit function.

3. **Constraints:**
   - **Space Constraint:** The total area used for growing both lettuce and tomatoes cannot exceed 300 sqft. This can be represented as \(L + T \leq 300\).
   - **Bug Spray Constraint:** For each square foot of lettuce, 5 mL of bug spray is needed, and for each square foot of tomatoes, 7 mL is needed. The total available bug spray is 255 mL. Therefore, this constraint can be represented as \(5L + 7T \leq 255\).
   - **Non-Negativity Constraint:** Since the gardener cannot grow a negative amount of lettuce or tomatoes, both \(L\) and \(T\) must be greater than or equal to 0.

Given these elements, we can now formulate the problem in Gurobi code as follows:

```python
from gurobipy import *

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

# Define the decision variables
L = m.addVar(lb=0, name="Lettuce")
T = m.addVar(lb=0, name="Tomatoes")

# Set the objective function to maximize profit
m.setObjective(2*L + 3*T, GRB.MAXIMIZE)

# Add constraints
m.addConstr(L + T <= 300, name="Space_Constraint")
m.addConstr(5*L + 7*T <= 255, name="Bug_Spray_Constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal Solution Found:")
    print(f"Lettuce: {L.x} sqft")
    print(f"Tomatoes: {T.x} sqft")
    print(f"Maximum Profit: ${2*L.x + 3*T.x}")
else:
    print("No optimal solution found")

```