To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's denote the number of acres for peaches as $x_1$ and the number of acres for nectarines as $x_2$. 

The objective function aims to maximize profit. Given that the profit per acre of peaches is $200 and the profit per acre of nectarines is $175, we can represent the total profit as $200x_1 + 175x_2$.

Now, let's consider the constraints:
1. The farmer has a limited amount of time for planting: 3 hours per acre for peaches and 4.5 hours per acre for nectarines, with a total of 300 hours available. This constraint can be represented as $3x_1 + 4.5x_2 \leq 300$.
2. The farmer also has a limited amount of time for watering: 2 hours per acre for peaches and 3 hours per acre for nectarines, with a total of 250 hours available. This constraint can be represented as $2x_1 + 3x_2 \leq 250$.
3. The farmer has a total of 80 acres available for both peaches and nectarines. This constraint can be represented as $x_1 + x_2 \leq 80$.
4. Additionally, the number of acres for each cannot be negative: $x_1 \geq 0$ and $x_2 \geq 0$.

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'acres of peaches'), ('x2', 'acres of nectarines')],
    'objective_function': '200*x1 + 175*x2',
    'constraints': [
        '3*x1 + 4.5*x2 <= 300',
        '2*x1 + 3*x2 <= 250',
        'x1 + x2 <= 80',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

To solve this optimization problem using Gurobi in Python, we'll use the following code:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="acres_of_peaches")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="acres_of_nectarines")

# Set objective function: Maximize profit
m.setObjective(200*x1 + 175*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(3*x1 + 4.5*x2 <= 300, "planting_time_constraint")
m.addConstr(2*x1 + 3*x2 <= 250, "watering_time_constraint")
m.addConstr(x1 + x2 <= 80, "total_acres_constraint")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal acres of peaches: {x1.x}")
    print(f"Optimal acres of nectarines: {x2.x}")
    print(f"Maximum profit: ${200*x1.x + 175*x2.x:.2f}")
else:
    print("No optimal solution found")
```