## Problem Description and Formulation

The problem described is a classic example of a linear programming problem. Bob has 100 acres of land and a budget of $3000 to plant daisies and peonies. The goal is to maximize profit given the costs and profits per acre for each type of flower.

Let's define the variables:
- \(x\): The number of acres to plant daisies.
- \(y\): The number of acres to plant peonies.

The constraints are:
1. Land constraint: \(x + y \leq 100\)
2. Budget constraint: \(20x + 35y \leq 3000\)
3. Non-negativity constraints: \(x \geq 0, y \geq 0\)

The objective function to maximize profit is: \(55x + 80y\)

## Gurobi Code

To solve this problem using Gurobi in Python, we will first install the Gurobi library if it's not already installed. You can install it using pip:

```bash
pip install gurobi
```

Now, let's formulate and solve the problem:

```python
import gurobi as gp

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

# Define variables
x = model.addVar(lb=0, name="daisies")  # Acres of daisies
y = model.addVar(lb=0, name="peonies")  # Acres of peonies

# Objective function: Maximize profit
model.setObjective(55*x + 80*y, gp.GRB.MAXIMIZE)

# Land constraint
model.addConstr(x + y <= 100, name="land_constraint")

# Budget constraint
model.addConstr(20*x + 35*y <= 3000, name="budget_constraint")

# Solve the model
model.optimize()

# Print solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal acres of daisies: {x.varValue}")
    print(f"Optimal acres of peonies: {y.varValue}")
    print(f"Maximal profit: ${model.objVal:.2f}")
else:
    print("The problem is infeasible")
```