## Symbolic Representation

The problem can be converted into a symbolic representation as follows:

Let's denote:
- $x_1$ as the acres of carrots
- $x_2$ as the acres of green peas

The objective function to maximize profit is:
\[ \text{Maximize:} \quad 250x_1 + 340x_2 \]

The constraints based on the given resources are:
- Watering days: $0.7x_1 + 0.4x_2 \leq 135$
- Spraying days: $1.2x_1 + 1.5x_2 \leq 110$
- Acres of farmland: $x_1 + x_2 \leq 100$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'acres of carrots'), ('x2', 'acres of green peas')],
    'objective_function': '250*x1 + 340*x2',
    'constraints': [
        '0.7*x1 + 0.4*x2 <= 135',
        '1.2*x1 + 1.5*x2 <= 110',
        'x1 + x2 <= 100',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define the variables
x1 = model.addVar(name="carrots", lb=0, ub=100, obj=250)  # Acres of carrots
x2 = model.addVar(name="green_peas", lb=0, ub=100, obj=340)  # Acres of green peas

# Define the constraints
model.addConstr(0.7 * x1 + 0.4 * x2 <= 135, name="watering_days")
model.addConstr(1.2 * x1 + 1.5 * x2 <= 110, name="spraying_days")
model.addConstr(x1 + x2 <= 100, name="farmland")

# Set the model to maximize
model.ModelSense = -1

# Solve the model
model.optimize()

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