## Step 1: Define the symbolic representation of the problem

Let's denote the acres of peaches as $x_1$ and the acres of nectarines as $x_2$. The objective is to maximize profit, which can be represented as $200x_1 + 175x_2$. The constraints based on the given information are:

- $x_1 + x_2 \leq 80$ (total acres constraint)
- $3x_1 + 4.5x_2 \leq 300$ (planting hours constraint)
- $2x_1 + 3x_2 \leq 250$ (watering hours constraint)
- $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity constraints)

## Step 2: Convert the problem into a symbolic representation

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

## Step 3: Implement the problem in Gurobi code

Now, let's implement this problem using Gurobi in Python:

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='x1', lb=0, ub=80, obj=200)  # acres of peaches
    x2 = model.addVar(name='x2', lb=0, ub=80, obj=175)  # acres of nectarines

    # Add constraints
    model.addConstr(x1 + x2 <= 80, name='total_acres')
    model.addConstr(3*x1 + 4.5*x2 <= 300, name='planting_hours')
    model.addConstr(2*x1 + 3*x2 <= 250, name='watering_hours')

    # Set the model to maximize the objective function
    model.setObjective(x1.obj * x1.varValue + x2.obj * x2.varValue, gurobi.GRB.MAXIMIZE)
    model.update()

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: Acres of peaches = {x1.x}, Acres of nectarines = {x2.x}")
        print(f"Maximum profit: ${200*x1.x + 175*x2.x:.2f}")
    else:
        print("The model is infeasible")

solve_fruit_farmer_problem()
```

However, it seems there was an attempt to directly use `x1.obj` and `x1.varValue` which are not valid in this context as `obj` and `varValue` are attributes that should be accessed via the variable object correctly and at the right time. The right approach should involve directly using Gurobi's Python API correctly to define the model, its objective, and constraints.

## 4: Correct the Gurobi Implementation

Here is the corrected version:

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='x1', lb=0, ub=80)  # acres of peaches
    x2 = model.addVar(name='x2', lb=0, ub=80)  # acres of nectarines

    # Set the objective function
    model.setObjective(200*x1 + 175*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(x1 + x2 <= 80, name='total_acres')
    model.addConstr(3*x1 + 4.5*x2 <= 300, name='planting_hours')
    model.addConstr(2*x1 + 3*x2 <= 250, name='watering_hours')

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: Acres of peaches = {x1.x}, Acres of nectarines = {x2.x}")
        print(f"Maximum profit: ${200*x1.x + 175*x2.x:.2f}")
    else:
        print("The model is infeasible")

solve_fruit_farmer_problem()
```