## Step 1: Define the symbolic representation of the problem

Let's denote:
- $x_1$ as the sqft of lettuce
- $x_2$ as the sqft of tomatoes

The profit per sqft of lettuce is $2, and the profit per sqft of tomatoes is $3. Therefore, the objective function to maximize profit is:
\[ \text{Maximize:} \quad 2x_1 + 3x_2 \]

## Step 2: Define the constraints

1. The gardener has a 300 sqft backyard:
\[ x_1 + x_2 \leq 300 \]

2. The gardener has 255 mL of bug spray available:
- 5 mL of bug spray per sqft of lettuce
- 7 mL of bug spray per sqft of tomatoes
\[ 5x_1 + 7x_2 \leq 255 \]

3. Non-negativity constraints (the gardener cannot grow a negative amount of lettuce or tomatoes):
\[ x_1 \geq 0, x_2 \geq 0 \]

## Step 3: Symbolic representation in JSON format

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

## Step 4: Gurobi code in Python

```python
import gurobi

def solve_gardening_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name='lettuce', lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name='tomatoes', lb=0, ub=gurobi.GRB.INFINITY)

    # Objective function: Maximize 2*x1 + 3*x2
    model.setObjective(2*x1 + 3*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 300, name='backyard_constraint')
    model.addConstr(5*x1 + 7*x2 <= 255, name='bug_spray_constraint')

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal sqft of lettuce: {x1.varValue}")
        print(f"Optimal sqft of tomatoes: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("The model is infeasible")

solve_gardening_problem()
```