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

**Decision Variables:**

* `x`: Acres of carrots planted
* `y`: Acres of pumpkins planted

**Objective Function:**

Maximize profit:  `80x + 124y`

**Constraints:**

* **Land Constraint:** `x + y <= 1500` (Total acres cannot exceed 1500)
* **Tractor Time Constraint:** `15x + 20y <= 1000` (Total tractor hours cannot exceed 1000)
* **Capital Constraint:** `12x + 55y <= 25000` (Total capital spent cannot exceed $25000)
* **Non-negativity Constraints:** `x >= 0`, `y >= 0` (Cannot plant negative acres)


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

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

# Create decision variables
x = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="carrots") # Acres of carrots
y = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="pumpkins") # Acres of pumpkins

# Set objective function
m.setObjective(80*x + 124*y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x + y <= 1500, "Land_Constraint")
m.addConstr(15*x + 20*y <= 1000, "Tractor_Constraint")
m.addConstr(12*x + 55*y <= 25000, "Capital_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 carrots")
    print(f"Plant {y.x:.2f} acres of pumpkins")
    print(f"Maximum Profit: ${m.objVal:.2f}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
