To solve this linear programming optimization problem, we first need to define the decision variables and the objective function. Let's denote:

- \(x\) as the number of acres for growing peaches,
- \(y\) as the number of acres for growing nectarines.

The objective is to maximize profit. The profit per acre from peaches is $200, and from nectarines is $175. Thus, the total profit can be represented by the equation \(200x + 175y\).

We have constraints based on the time available for planting and watering, as well as the total land available:

1. **Planting Time Constraint**: Peaches take 3 hours to plant per acre, and nectarines take 4.5 hours to plant per acre. The farmer has 300 hours available for planting. So, \(3x + 4.5y \leq 300\).

2. **Watering Time Constraint**: Peaches take 2 hours to water per acre, and nectarines take 3 hours to water per acre. The farmer has 250 hours available for watering. Thus, \(2x + 3y \leq 250\).

3. **Land Availability Constraint**: The farmer has a total of 80 acres available. Therefore, \(x + y \leq 80\).

4. **Non-Negativity Constraints**: Since the number of acres cannot be negative, we have \(x \geq 0\) and \(y \geq 0\).

Now, let's translate these constraints into Gurobi code in Python:

```python
from gurobipy import *

# Create a model
m = Model("Fruit_Farmer_Optimization")

# Define the decision variables
x = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="peaches_acres")
y = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="nectarines_acres")

# Objective function: Maximize profit
m.setObjective(200*x + 175*y, GRB.MAXIMIZE)

# Constraints
m.addConstr(3*x + 4.5*y <= 300, "planting_time")
m.addConstr(2*x + 3*y <= 250, "watering_time")
m.addConstr(x + y <= 80, "land_availability")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print(f"Optimal acres of peaches: {x.x}")
    print(f"Optimal acres of nectarines: {y.x}")
    print(f"Maximum profit: ${200*x.x + 175*y.x}")
else:
    print("Model is infeasible")
```