To solve this optimization problem, we first need to convert the natural language description into a symbolic representation. Let's define:

- $x_1$ as the area in square feet of lettuce grown.
- $x_2$ as the area in square feet of tomatoes grown.

The objective is to maximize profit. Given that the profit per square foot of lettuce is $2 and the profit per square foot of tomatoes is $3, we can represent the total profit (objective function) as:

\[ \text{Maximize:} \quad 2x_1 + 3x_2 \]

The constraints are:
1. The total area used cannot exceed 300 sqft.
\[ x_1 + x_2 \leq 300 \]
2. The bug spray constraint: per square foot of lettuce requires 5 mL, and per square foot of tomatoes requires 7 mL, with a total of 255 mL available.
\[ 5x_1 + 7x_2 \leq 255 \]
3. Non-negativity constraints since area cannot be negative:
\[ x_1 \geq 0 \]
\[ x_2 \geq 0 \]

Thus, the symbolic representation of the problem is:

```json
{
    'sym_variables': [('x1', 'lettuce'), ('x2', 'tomatoes')],
    'objective_function': '2*x1 + 3*x2',
    'constraints': [
        'x1 + x2 <= 300',
        '5*x1 + 7*x2 <= 255',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

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

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, name="lettuce")
x2 = m.addVar(lb=0, name="tomatoes")

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

# Add constraints
m.addConstr(x1 + x2 <= 300, "total_area")
m.addConstr(5*x1 + 7*x2 <= 255, "bug_spray")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Lettuce: {x1.x} sqft")
    print(f"Tomatoes: {x2.x} sqft")
    print(f"Total Profit: ${m.objVal}")
else:
    print("No optimal solution found.")
```