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 green peas planted

**Objective Function:**

Maximize profit: `250x + 340y`

**Constraints:**

* **Land Constraint:** `x + y <= 100` (Total acres cannot exceed 100)
* **Watering Constraint:** `0.7x + 0.4y <= 135` (Total watering days cannot exceed 135)
* **Spraying Constraint:** `1.2x + 1.5y <= 110` (Total spraying days cannot exceed 110)
* **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 variables
x = m.addVar(lb=0, name="carrots") # Acres of carrots
y = m.addVar(lb=0, name="green_peas") # Acres of green peas

# Set objective function
m.setObjective(250*x + 340*y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x + y <= 100, "land_constraint")
m.addConstr(0.7*x + 0.4*y <= 135, "watering_constraint")
m.addConstr(1.2*x + 1.5*y <= 110, "spraying_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 green peas")
    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}")

```
