Here's how we can formulate this problem and the corresponding Gurobi code:

**Decision Variables:**

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

**Objective Function:**

Maximize profit: `300x + 500y`

**Constraints:**

* **Land Availability:** `x + y <= 200`
* **Carrot Preference:** `x >= y`
* **Soil/Weather:** `x <= 2y`
* **Minimum Carrots:** `x >= 25`
* **Minimum Pumpkins:** `y >= 20`
* **Non-negativity:** `x, y >= 0`


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

# Create a new model
m = gp.Model("Luke's Farm")

# Create variables
x = m.addVar(lb=0, name="carrots") # hectares of carrots
y = m.addVar(lb=0, name="pumpkins") # hectares of pumpkins

# Set objective function
m.setObjective(300*x + 500*y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x + y <= 200, "land_availability")
m.addConstr(x >= y, "carrot_preference")
m.addConstr(x <= 2*y, "soil_weather")
m.addConstr(x >= 25, "min_carrots")
m.addConstr(y >= 20, "min_pumpkins")

# Optimize model
m.optimize()

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

```
