## Problem Description and Formulation

The problem is a classic example of a linear programming (LP) problem. The goal is to maximize the profit of Bob's berry farm by deciding how many acres to plant with cranberries and bilberries, given constraints on labor and watering costs.

Let's define the decision variables:

* `x`: acres of cranberries
* `y`: acres of bilberries

The objective function to maximize is the total profit:

* Profit per acre of cranberries: $66
* Profit per acre of bilberries: $73
* Total profit: `66x + 73y`

The constraints are:

* Labor: 5 days/acre of cranberries, 4 days/acre of bilberries, and 600 days available: `5x + 4y <= 600`
* Watering costs: $25/acre of cranberries, $30/acre of bilberries, and $9000 available: `25x + 30y <= 9000`
* Acres of land: 250 acres available: `x + y <= 250`
* Non-negativity: `x >= 0`, `y >= 0`

## Gurobi Code

```python
import gurobi

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

# Define the decision variables
x = model.addVar(lb=0, name="cranberries")  # acres of cranberries
y = model.addVar(lb=0, name="bilberries")  # acres of bilberries

# Define the objective function
model.setObjective(66 * x + 73 * y, gurobi.GRB.MAXIMIZE)

# Define the constraints
model.addConstr(5 * x + 4 * y <= 600, name="labor")  # labor constraint
model.addConstr(25 * x + 30 * y <= 9000, name="watering")  # watering constraint
model.addConstr(x + y <= 250, name="land")  # land constraint

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution:")
    print(f"Cranberries: {x.varValue} acres")
    print(f"Bilberries: {y.varValue} acres")
    print(f"Max profit: ${model.objVal:.2f}")
else:
    print("No optimal solution found")
```