## Step 1: Define the symbolic representation of the problem
Let's denote the number of acres for daisies as $x_1$ and for peonies as $x_2$. The objective is to maximize profit, which is $55x_1 + 80x_2$. The constraints are:
1. $x_1 + x_2 \leq 100$ (total acres of land),
2. $20x_1 + 35x_2 \leq 3000$ (budget constraint),
3. $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity constraints).

## Step 2: Convert the problem into a symbolic representation
The symbolic representation can be described as:
```json
{
'sym_variables': [('x1', 'acres of daisies'), ('x2', 'acres of peonies')],
'objective_function': '55*x1 + 80*x2',
'constraints': [
    'x1 + x2 <= 100',
    '20*x1 + 35*x2 <= 3000',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 3: Translate the symbolic representation into Gurobi code
To solve this linear programming problem using Gurobi, we will use the Gurobi Python API.

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(lb=0, name="daisies")
    x2 = model.addVar(lb=0, name="peonies")

    # Set the objective function
    model.setObjective(55 * x1 + 80 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(x1 + x2 <= 100, name="land_constraint")
    model.addConstr(20 * x1 + 35 * x2 <= 3000, name="budget_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Acres of daisies: {x1.varValue}")
        print(f"Acres of peonies: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```