## Problem Description and Formulation

The problem is a classic example of a linear programming problem. The goal is to maximize the profit of a fruit farmer who has 80 acres to grow peaches and nectarines. The farmer has limited hours available for planting and watering.

Let's define the decision variables:

* $x$: acres of peaches
* $y$: acres of nectarines

The objective function is to maximize the profit:

* Profit per acre of peaches: $200
* Profit per acre of nectarines: $175

The constraints are:

* Total acres: $x + y \leq 80$
* Planting hours: $3x + 4.5y \leq 300$
* Watering hours: $2x + 3y \leq 250$
* Non-negativity: $x \geq 0, y \geq 0$

## Gurobi Code

```python
import gurobi

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

    # Define the decision variables
    x = model.addVar(lb=0, name="acres_of_peaches")
    y = model.addVar(lb=0, name="acres_of_nectarines")

    # Set the objective function
    model.setObjective(200 * x + 175 * y, gurobi.GRB.MAXIMIZE)

    # Add the constraints
    model.addConstr(x + y <= 80, name="total_acres")
    model.addConstr(3 * x + 4.5 * y <= 300, name="planting_hours")
    model.addConstr(2 * x + 3 * y <= 250, name="watering_hours")

    # Optimize the model
    model.optimize()

    # Check if the model is infeasible
    if model.status == gurobi.GRB.INFEASIBLE:
        print("The problem is infeasible.")
        return

    # Print the solution
    print(f"Acres of peaches: {x.varValue}")
    print(f"Acres of nectarines: {y.varValue}")
    print(f"Max profit: {model.objVal}")

solve_fruit_farmer_problem()
```