Here's the formulation of the linear program and the Gurobi code to solve it:

**Decision Variables:**

* `x`: Acres of apple trees to plant
* `y`: Acres of peach trees to plant

**Objective Function:**

Maximize profit:  15x + 25y

**Constraints:**

* **Land Constraint:** x + y <= 4000  (Total acres available)
* **Budget Constraint:** 50x + 80y <= 30000 (Sapling budget)
* **Maintenance Constraint:** 3x + 5y <= 600 (Available maintenance hours)
* **Non-negativity Constraints:** x >= 0, y >= 0

```python
import gurobipy as gp
from gurobipy import GRB

# Create a new model
m = gp.Model("Orchard_Optimization")

# Create decision variables
x = m.addVar(lb=0, name="apple_acres")
y = m.addVar(lb=0, name="peach_acres")

# Set objective function
m.setObjective(15*x + 25*y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x + y <= 4000, "land_constraint")
m.addConstr(50*x + 80*y <= 30000, "budget_constraint")
m.addConstr(3*x + 5*y <= 600, "maintenance_constraint")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Plant {x.x:.2f} acres of apple trees")
    print(f"Plant {y.x:.2f} acres of peach trees")
    print(f"Maximum Profit: ${m.objVal:.2f}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible. No solution exists.")
else:
    print(f"Optimization terminated with status {m.status}")

```
