To solve this optimization problem, we first need to define the decision variables and the objective function. Let's denote the number of acres dedicated to growing beans as \(B\) and the number of acres for peas as \(P\). The profit per acre from beans is $200, and from peas is $250. We aim to maximize the total profit.

The objective function can be written as:
\[ \text{Maximize:} \quad 200B + 250P \]

We have two main constraints:
1. **Total Acreage**: The total number of acres used for beans and peas cannot exceed 100 acres.
\[ B + P \leq 100 \]
2. **Bug Repellant**: The amount of bug repellant used must not exceed 1350 liters. Given that beans require 12 liters per acre and peas require 15 liters per acre, we have:
\[ 12B + 15P \leq 1350 \]

Additionally, \(B\) and \(P\) must be non-negative since they represent acres of land.

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

```python
from gurobipy import *

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

# Define the decision variables
B = m.addVar(name='beans', vtype=GRB.CONTINUOUS, lb=0)
P = m.addVar(name='peas', vtype=GRB.CONTINUOUS, lb=0)

# Set the objective function
m.setObjective(200*B + 250*P, GRB.MAXIMIZE)

# Add constraints
m.addConstr(B + P <= 100, name='total_acreage')
m.addConstr(12*B + 15*P <= 1350, name='bug_repellant')

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution found. Acres of beans: {B.x}, Acres of peas: {P.x}")
    print(f"Maximum profit: ${200*B.x + 250*P.x:.2f}")
else:
    print("No optimal solution found")
```