## Symbolic Representation

The problem can be represented symbolically as follows:

Let's define the variables:
- $x_1$ as the number of bouquets of daisies
- $x_2$ as the number of bouquets of lilies

The objective function to maximize profit is:
\[ \text{Maximize:} \quad 5x_1 + 4x_2 \]

The constraints based on the given limitations are:
1. Clipping time: $3x_1 + x_2 \leq 1000$
2. Packaging time: $2x_1 + 3x_2 \leq 650$
3. Minimum daisies: $x_1 \geq 25$
4. Non-negativity: $x_1 \geq 0, x_2 \geq 0$

However, since $x_1$ and $x_2$ represent the number of bouquets, they should naturally be non-negative, so we explicitly state $x_1 \geq 25$ but ensure $x_2$ is just non-negative.

## Gurobi Code

To solve this linear programming problem using Gurobi in Python, we can use the following code:

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=25, name="daisies")  # At least 25 bouquets of daisies
    x2 = model.addVar(lb=0, name="lilies")   # Non-negative bouquets of lilies

    # Define the objective function
    model.setObjective(5 * x1 + 4 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3 * x1 + x2 <= 1000, name="clipping_time")
    model.addConstr(2 * x1 + 3 * x2 <= 650, name="packaging_time")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Number of bouquets of daisies: {x1.varValue}")
        print(f"Number of bouquets of lilies: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("The model is infeasible")

solve_garden_problem()
```

## Full Solution in Requested Format

```json
{
  'sym_variables': [('x1', 'bouquets of daisies'), ('x2', 'bouquets of lilies')],
  'objective_function': 'Maximize: 5x1 + 4x2',
  'constraints': [
    '3x1 + x2 <= 1000 (clipping time)',
    '2x1 + 3x2 <= 650 (packaging time)',
    'x1 >= 25 (minimum daisies)',
    'x1, x2 >= 0 (non-negativity)'
  ]
}
```

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=25, name="daisies")  # At least 25 bouquets of daisies
    x2 = model.addVar(lb=0, name="lilies")   # Non-negative bouquets of lilies

    # Define the objective function
    model.setObjective(5 * x1 + 4 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3 * x1 + x2 <= 1000, name="clipping_time")
    model.addConstr(2 * x1 + 3 * x2 <= 650, name="packaging_time")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Number of bouquets of daisies: {x1.varValue}")
        print(f"Number of bouquets of lilies: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("The model is infeasible")

solve_garden_problem()
```