## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation.

Let's define the symbolic variables as follows:
- `x1`: acres of beans
- `x2`: acres of peas

The objective is to maximize profit. The profit per acre of beans is $200, and the profit per acre of peas is $250. Therefore, the objective function can be represented as:

`200*x1 + 250*x2`

The constraints are:
1. The total acres of beans and peas cannot exceed 100 acres: `x1 + x2 <= 100`
2. The total liters of bug repellant used cannot exceed 1350 liters: `12*x1 + 15*x2 <= 1350`
3. The acres of beans and peas cannot be negative: `x1 >= 0`, `x2 >= 0`

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'acres of beans'), ('x2', 'acres of peas')],
    'objective_function': '200*x1 + 250*x2',
    'constraints': [
        'x1 + x2 <= 100',
        '12*x1 + 15*x2 <= 1350',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Gardener_Problem")

# Define the variables
x1 = model.addVar(name="beans", lb=0, ub=100, vtype=gp.GRB.CONTINUOUS)  # acres of beans
x2 = model.addVar(name="peas", lb=0, ub=100, vtype=gp.GRB.CONTINUOUS)  # acres of peas

# Define the objective function
model.setObjective(200*x1 + 250*x2, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(x1 + x2 <= 100, name="total_acres")
model.addConstr(12*x1 + 15*x2 <= 1350, name="bug_repellant")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal acres of beans: {x1.varValue}")
    print(f"Optimal acres of peas: {x2.varValue}")
    print(f"Maximum profit: {model.objVal}")
else:
    print("The model is infeasible.")
```