To solve the given optimization problem, we need to maximize the profit from growing carrots and green peas on 100 acres of farmland. The constraints include limitations on watering and spraying days.

Let's denote:
- \(x\) as the number of acres for carrots,
- \(y\) as the number of acres for green peas.

The objective function (profit) can be represented as:
\[ \text{Maximize:} \quad 250x + 340y \]

Constraints are:
1. Total land used does not exceed 100 acres:
\[ x + y \leq 100 \]
2. Watering days constraint:
\[ 0.7x + 0.4y \leq 135 \]
3. Spraying bug spray days constraint:
\[ 1.2x + 1.5y \leq 110 \]
4. Non-negativity constraints ( acres cannot be negative ):
\[ x \geq 0, y \geq 0 \]

Now, let's translate this into Gurobi code in Python:

```python
from gurobipy import *

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

# Define variables
x = m.addVar(lb=0, name="carrots_acres")
y = m.addVar(lb=0, name="peas_acres")

# Objective function: Maximize profit
m.setObjective(250*x + 340*y, GRB.MAXIMIZE)

# Constraints
m.addConstr(x + y <= 100, "land_constraint")
m.addConstr(0.7*x + 0.4*y <= 135, "watering_days")
m.addConstr(1.2*x + 1.5*y <= 110, "spraying_days")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Carrots acres: {x.x}")
    print(f"Green peas acres: {y.x}")
    print(f"Max Profit: ${250*x.x + 340*y.x:.2f}")
else:
    print("No optimal solution found")
```