To solve Amanda's optimization problem, we first need to convert the natural language description into a symbolic representation. Let's denote the number of acres for apple trees as \(x_1\) and the number of acres for peach trees as \(x_2\).

The symbolic representation of the problem is as follows:
```json
{
  'sym_variables': [('x1', 'acres of apple trees'), ('x2', 'acres of peach trees')],
  'objective_function': '15*x1 + 25*x2',
  'constraints': [
    '50*x1 + 80*x2 <= 30000',  # Budget constraint for saplings
    '3*x1 + 5*x2 <= 600',      # Maintenance hours constraint
    'x1 + x2 <= 4000',         # Total land constraint
    'x1 >= 0',                 # Non-negativity constraint for apple trees
    'x2 >= 0'                  # Non-negativity constraint for peach trees
  ]
}
```

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

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(15*x1 + 25*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(50*x1 + 80*x2 <= 30000, "budget_constraint")
m.addConstr(3*x1 + 5*x2 <= 600, "maintenance_hours_constraint")
m.addConstr(x1 + x2 <= 4000, "total_land_constraint")

# 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 peach trees: {x2.x}")
    print(f"Maximum profit: ${15*x1.x + 25*x2.x:.2f}")
else:
    print("No optimal solution found.")
```