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

Let's denote the acres of apple trees as $x_1$ and the acres of orange trees as $x_2$. The objective is to maximize profit, which can be represented as $500x_1 + 450x_2$. The constraints are:
- $x_1 + x_2 \leq 80$ (total acres of land constraint)
- $30x_1 + 25x_2 \leq 2200$ (special soil 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 described as:
```json
{
    'sym_variables': [('x1', 'acres of apple trees'), ('x2', 'acres of orange trees')],
    'objective_function': '500*x1 + 450*x2',
    'constraints': [
        'x1 + x2 <= 80',
        '30*x1 + 25*x2 <= 2200',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 3: Implement the problem in Gurobi code

Now, let's implement this problem in Gurobi using Python:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='apple_trees', lb=0, ub=80, obj=500)
    x2 = model.addVar(name='orange_trees', lb=0, ub=80, obj=450)

    # Add constraints
    model.addConstr(x1 + x2 <= 80, name='land_constraint')
    model.addConstr(30*x1 + 25*x2 <= 2200, name='soil_constraint')

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

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'Acres of apple trees: {x1.varValue}')
        print(f'Acres of orange trees: {x2.varValue}')
        print(f'Maximum profit: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_farmer_problem()
```
However, the code above directly uses `x1.varValue` and `x2.varValue` which are not proper ways to get variable values in Gurobi. The correct way is by accessing the `x` attribute of the variable after optimization.

## 4: Correct the Gurobi Code

Here is the corrected version:
```python
import gurobi as grb

def solve_farmer_problem():
    # Create a new model
    model = grb.Model()

    # Define the variables
    x1 = model.addVar(name='apple_trees', lb=0, ub=80, obj=500)
    x2 = model.addVar(name='orange_trees', lb=0, ub=80, obj=450)

    # Add constraints
    model.addConstr(x1 + x2 <= 80, name='land_constraint')
    model.addConstr(30*x1 + 25*x2 <= 2200, name='soil_constraint')

    # Set the model to maximize the objective function
    model.optimize()

    # Print the results
    if model.status == grb.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'Acres of apple trees: {x1.x}')
        print(f'Acres of orange trees: {x2.x}')
        print(f'Maximum profit: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_farmer_problem()
```