## Problem Description and Formulation

The problem is a classic example of a linear programming problem. The family has 100 acres of farmland to allocate between carrots and green peas. The goal is to maximize profit, given the constraints on watering and spraying bug repellant.

Let's define the decision variables:

* `x`: acres of carrots
* `y`: acres of green peas

The objective function is to maximize profit:

* Profit = $250x + $340y

The constraints are:

* Land: `x + y <= 100` (total acres of farmland)
* Watering: `0.7x + 0.4y <= 135` (total days available for watering)
* Spraying: `1.2x + 1.5y <= 110` (total days available for spraying)
* Non-negativity: `x >= 0`, `y >= 0` (acres cannot be negative)

## Gurobi Code

```python
import gurobi

# Create a new Gurobi model
m = gurobi.Model()

# Define the decision variables
x = m.addVar(lb=0, name="carrots")  # acres of carrots
y = m.addVar(lb=0, name="green_peas")  # acres of green peas

# Define the objective function
m.setObjective(250*x + 340*y, gurobi.GRB.MAXIMIZE)

# Define the constraints
m.addConstr(x + y <= 100, name="land")
m.addConstr(0.7*x + 0.4*y <= 135, name="watering")
m.addConstr(1.2*x + 1.5*y <= 110, name="spraying")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution:")
    print(f"Acres of carrots: {x.varValue:.2f}")
    print(f"Acres of green peas: {y.varValue:.2f}")
    print(f"Max profit: ${m.objVal:.2f}")
else:
    print("No optimal solution found")
```