To solve this optimization problem, we first need to convert the natural language description into a symbolic representation. 

Let's define:
- $x_1$ as the number of acres of apple trees.
- $x_2$ as the number of acres of orange trees.

The objective function is to maximize profit, which can be represented algebraically as:
\[500x_1 + 450x_2\]

The constraints are:
1. The total amount of special soil used does not exceed 2200 kg: 
\[30x_1 + 25x_2 \leq 2200\]
2. The total land used does not exceed 80 acres:
\[x_1 + x_2 \leq 80\]
3. Non-negativity constraints (since we cannot have negative acres of trees):
\[x_1 \geq 0, x_2 \geq 0\]

In symbolic notation with natural language objects, the problem can be represented as:
```json
{
'sym_variables': [('x1', 'acres of apple trees'), ('x2', 'acres of orange trees')], 
'objective_function': '500*x1 + 450*x2', 
'constraints': ['30*x1 + 25*x2 <= 2200', 'x1 + x2 <= 80', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's write the Gurobi code in Python to solve this optimization problem:

```python
from gurobipy import *

# Create a new model
m = Model("Farmer_Profit")

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

# Set the objective function
m.setObjective(500*x1 + 450*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(30*x1 + 25*x2 <= 2200, "soil_limit")
m.addConstr(x1 + x2 <= 80, "land_limit")

# Optimize the model
m.optimize()

# Print the results
if m.status == 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: ${m.objVal:.2f}")
else:
    print("No optimal solution found")
```